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NVIDIA TAO Toolkit provides a low-code AI framework to accelerate vision AI model development suitable for all skill levels, from novice beginners to expert…

NVIDIA TAO Toolkit provides a low-code AI framework to accelerate vision AI model development suitable for all skill levels, from novice beginners to expert data scientists. With the TAO Toolkit, developers can use the power and efficiency of transfer learning to achieve state-of-the-art accuracy and production-class throughput in record time with adaptation and optimization.  

NVIDIA released TAO Toolkit 5.0, bringing groundbreaking features to enhance any AI model development. The new features include source-open architecture, transformer-based pretrained models, AI-assisted data annotation, and the capability to deploy models on any platform.

Release highlights include:

• Model export in open ONNX format to support deployment on GPUs, CPUs, MCUs, neural accelerators, ‌and more.
• Advanced Vision Transformer training for better accuracy and robustness against image corruption and noise.
• New AI-assisted data annotation, accelerating labeling tasks for segmentation masks.
• Support for new computer vision tasks and pretrained models for optical inspection, such as optical character detection and Siamese Network models.
• Open source availability for customizable solutions, faster development, and integration.

Get started

• Visit the TAO Toolkit getting started page for instructional videos and quick start guides.
• Download TAO Toolkit and pretrained models from NGC.

This post has been revised from its original version to provide accurate information reflecting the TAO Toolkit 5.0 release.

Deploy NVIDIA TAO models on any platform, anywhere 

NVIDIA TAO Toolkit 5.0 supports model export in ONNX. This makes it possible to deploy a model trained with NVIDIA TAO Toolkit on any computing platform—GPUs, CPUs, MCUs, DLAs, FPGAs—at the edge or in the cloud. NVIDIA TAO Toolkit simplifies the model training process and optimizes the model for inference throughput, powering AI across hundreds of billions of devices.  

Edge Impulse, a platform for building, refining, and deploying machine learning models and algorithms, integrated TAO Toolkit into their edge AI platform. With this integration, Edge Impulse now offers advanced vision AI capabilities and models that complement its current offerings. Developers can use the platform to build production AI with TAO for any edge device. Learn more about the integration in a blog post from Edge Impulse.

STMicroelectronics, a global leader in embedded microcontrollers, integrated NVIDIA TAO Toolkit into its STM32Cube AI developer workflow. This puts the latest AI capabilities into the hands of millions of STMicroelectronics developers. It provides, for the first time, the ability to integrate sophisticated AI into widespread IoT and edge use cases powered by the STM32Cube. 

Now, with NVIDIA TAO Toolkit, even the most novice AI developers can optimize and quantize AI models to run on STM32 MCU within the microcontroller’s compute and memory budget. Developers can also bring their own models and fine-tune using TAO Toolkit. More information about this work is captured in the following demo. Learn more about the project on the STMicroelectronics GitHub page.

While TAO Toolkit models can run on any platform, these models achieve the highest throughput on NVIDIA GPUs using TensorRT for inference. On CPUs, these models use ONNX-RT for inference. The script and recipe to replicate these numbers will be provided once the software becomes available.

AI-assisted data annotation and management 

Data annotation remains an expensive and time-consuming process for all AI projects. This is especially true for CV tasks like segmentation that require generating a segmentation mask at pixel level around the object. Generally, segmentation masks cost 10x more than object detection or classification. 

It is faster and less expensive to annotate segmentation masks with new AI-assisted annotation capabilities using TAO Toolkit 5.0. Now you can use the weakly supervised segmentation architecture, Mask Auto Labeler (MAL) to aid in segmentation annotation and in fixing and tightening bounding boxes for object detection. Loose bounding boxes around an object in ground truth data can lead to suboptimal detection results. But, with AI-assisted annotation, you can tighten your bounding boxes over objects, leading to more accurate models.  

MAL is a transformer-based, mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates the mask pseudo-labels. It uses COCO annotation format for both input and output labels.  

MAL significantly reduces the gap between auto labeling and human annotation for mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of the fully supervised counterparts, retaining up to 97.4% performance of fully supervised models.  

When training the MAL network, a task network and a teacher network (sharing the same transformer structure) work together to achieve class-agnostic self-training. This enables refining the prediction masks with conditional random field (CRF) loss and multi-instance learning (MIL) loss.    

TAO Toolkit uses MAL in both the auto-labeling pipeline and data augmentation pipeline. Specifically, users can generate pseudo-masks on the spatially augmented images (sheared or rotated, for example), and refine and tighten the corresponding bounding boxes using the generated masks. 

State-of-the-art Vision Transformers 

Transformers have become the standard architecture in NLP, largely because of self-attention. They have also gained popularity for a range of vision AI tasks. In general, transformer-based models can outperform traditional CNN-based models due to their robustness, generalizability, and ability to perform parallelized processing of large-scale inputs. All of this increases training efficiency, provides better robustness against image corruption and noise, and generalizes better on unseen objects.  

TAO Toolkit 5.0 features several state-of-the-art (SOTA) Vision Transformers for popular CV tasks, as detailed below.

Fully Attentional Network  

Fully Attentional Network (FAN) is a transformer-based family of backbones from NVIDIA Research that achieves SOTA in robustness against various corruptions. This family of backbones can easily generalize to new domains and be more robust to noise, blur, and more. 

A key design behind the FAN block is the attentional channel processing module that leads to robust representation learning. FAN can be used for image classification tasks as well as downstream tasks such as object detection and segmentation.  

The FAN family supports four backbones, as shown in Table 2.

Global Context Vision Transformer 

Global Context Vision Transformer (GC-ViT) is a novel architecture from NVIDIA Research that achieves very high accuracy and compute efficiency. GC-ViT addresses the lack of inductive bias in Vision Transformers. It achieves better results on ImageNet with a smaller number of parameters through the use of local self-attention. 

Local self-attention paired with global context self-attention can effectively and efficiently model both long and short-range spatial interactions. Figure 6 shows the GC-ViT model architecture. For more details, see Global Context Vision Transformers.

As shown in Table 3, the GC-ViT family contains six backbones, ranging from GC-ViT-xxTiny (compute efficient) to GC-ViT-Large (very accurate). GC-ViT-Large models can achieve Top-1 accuracy of 85.6 on the ImageNet-1K dataset for image classification tasks. This architecture can also be used as the backbone for other CV tasks like object detection and semantic and instance segmentation.  

DINO 

DINO (detection transformer with improved denoising anchor) is the newest generation of detection transformers (DETR). It achieves a faster training convergence time than its predecessor. Deformable-DETR (D-DETR) requires at least 50 epochs to converge, while DINO can converge in 12 epochs on the COCO dataset. It also achieves higher accuracy when compared with D-DETR. 

DINO achieves faster convergence through the use of denoising during training, which helps the bipartite matching process at the proposal generation stage. The training convergence of DETR-like models is slow due to the instability of bipartite matching. Bipartite matching removed the need for handcrafted and compute-heavy NMS operations. However, it often required much more training because incorrect ground truths were matched to the predictions during bipartite matching. 

To remedy such a problem, DINO introduced noised positive ground-truth boxes and negative ground-truth boxes to handle “no object” scenarios. As a result, training converges very quickly for DINO. For more information, see DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

DINO in TAO Toolkit is flexible and can be combined with various backbones from traditional CNNs, such as ResNets, and transformer-based backbones like FAN and GC-ViT. Table 4 shows the accuracy of the COCO dataset on various versions of DINO with popular YOLOv7. For more details, see YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors.

SegFormer 

SegFormer is a lightweight transformer-based semantic segmentation. The decoder is made of lightweight MLP layers. It avoids using positional encoding (mostly used by transformers), which makes the inference efficient at different resolutions.  

Adding FAN backbone to SegFormer MLP decoder results in a highly robust and efficient semantic segmentation model. FAN base hybrid + SegFormer was the winning architecture at the Robust Vision Challenge 2022 for semantic segmentation. 

See how SegFormer generates robust semantic segmentation while maintaining high efficiency for accelerated autonomous vehicle development in the following video. 

CV tasks beyond object detection and segmentation 

NVIDIA TAO Toolkit accelerates a wide range of CV tasks beyond traditional object detection and segmentation. The new character detection and recognition models in TAO Toolkit 5.0 enable developers to extract text from images and documents. This automates document conversion and accelerates use cases in industries like insurance and finance.  

Detecting anomalies in images is useful when the object being classified varies greatly, such that training with all the variations is impossible. In industrial inspection, for example, a defect can come in any form. Using a simple classifier could result in many missed defects if the defect has not been previously seen by the training data. 

For such use cases, comparing the test object directly against a golden reference would result in better accuracy. TAO Toolkit 5.0 features a Siamese neural network in which the model calculates the difference between the object under test and a golden reference to classify if the object is defective.  

Automate training using AutoML for hyperparameter optimization 

Automated machine learning (autoML) automates the manual task of finding the best models and hyperparameters for the desired KPI on a given dataset. It can algorithmically derive the best model and abstract away much of the complexity of AI model creation and optimization.  

AutoML in TAO Toolkit is fully configurable for automatically optimizing the hyperparameters of a model. It caters to both AI experts and nonexperts. For nonexperts, the guided Jupyter Notebook provides a simple, efficient way to create an accurate AI model. 

For experts, TAO Toolkit gives you full control of which hyperparameters to tune and which algorithm to use for sweeps. TAO Toolkit currently supports two optimization algorithms: Bayesian and Hyperband optimization. These algorithms can sweep across a range of hyperparameters to find the best combination for a given dataset. 

AutoML is supported for a wide range of CV tasks, including several new Vision Transformers such as DINO, D-DETR, SegFormer, and more. Table 6 shows the full list of supported networks (bold items are new to TAO Toolkit 5.0). 

REST APIs for workflow integration 

TAO Toolkit is modular and cloud-native, meaning it is available as containers and can be deployed and managed using Kubernetes. TAO Toolkit can be deployed as a self-managed service on any public or private cloud, DGX, or workstation. TAO Toolkit provides well-defined REST APIs, making it easy to integrate into your development workflow. Developers can call the API endpoints for all training and optimization tasks. These API endpoints can be called from any application or user interface, which can trigger training jobs remotely. 

Better inference optimization 

To simplify productization and increase inference throughput, TAO Toolkit provides several turnkey performance optimization techniques. These include model pruning, lower precision quantization, and TensorRT optimization, which can combine to deliver a 4x to 8x performance boost, compared to a comparable model from public model zoos. 

Open and flexible, with better support 

An AI model predicts output based on complex algorithms. This can make it difficult to understand how the system arrived at its decision and challenging to debug, diagnose, and fix errors. Explainable AI (XAI) aims to address these challenges by providing insights into how AI models arrive at their decisions. This helps humans understand the reasoning behind the AI output and makes it easier to diagnose and fix errors. This transparency can help to build trust in AI systems. 

To help with transparency and explainability, TAO Toolkit will now be available as source-open. Developers will be able to view feature maps from internal layers, as well as plot activation heat maps to better understand the reasoning behind AI predictions. In addition, having access to the source code will give developers the flexibility to create customized AI, improve debug capability, and increase trust in their models.  

NVIDIA TAO Toolkit is enterprise-ready and available through NVIDIA AI Enterprise (NVAIE). NVAIE provides companies with business-critical support, access to NVIDIA AI experts, and priority security fixes. Join NVAIE to get support from AI experts.  

Integration with cloud services 

NVIDIA TAO Toolkit 5.0 is integrated into various AI services that you might already use, such as Google Vertex AI, AzureML, Azure Kubernetes service, Google GKE, and Amazon EKS. 

Summary 

TAO Toolkit offers a platform for any developer, in any service, and on any device to easily transfer-learn their custom models, perform quantization and pruning, manage complex training workflows, and perform AI-assisted annotation with no coding requirements.

Vision Transformers (ViTs) are taking computer vision by storm, offering incredible accuracy, robust solutions for challenging real-world scenarios, and…

Vision Transformers (ViTs) are taking computer vision by storm, offering incredible accuracy, robust solutions for challenging real-world scenarios, and improved generalizability. The algorithms are playing a pivotal role in boosting computer vision applications and NVIDIA is making it easy to integrate ViTs into your applications using NVIDIA TAO Toolkit and NVIDIA L4 GPUs.

How ViTs are different

ViTs are machine learning models that apply transformer architectures, originally designed for natural language processing, to visual data. They have several advantages over their CNN-based counterparts and the ability to perform parallelized processing of large-scale inputs. While CNNs use local operations that lack a global understanding of an image, ViTs provide long-range dependencies and global context. They do this effectively by processing images in a parallel and self-attention-based manner, enabling interactions between all image patches. 

Figure 1 shows see the processing of an image in a ViT model, where the input image is divided into smaller fixed-size patches, which are flattened and transformed into sequences of tokens. These tokens, along with positional encodings, are then fed into a transformer encoder, which consists of multiple layers of self-attention and feed-forward neural networks. 

With the self-attention mechanism, each token or patch of an image interacts with other tokens to decide which tokens are important. This helps the model capture relationships and dependencies between tokens and learns which ones are considered important over others. 

For example, with an image of a bird, the model pays more attention to important features, such as the eyes, beak, and feathers rather than the background. This translates into increased training efficiency, enhanced robustness against image corruption and noise, and superior generalization on unseen objects.

Why ViTs are critical for computer vision applications

Real-world environments have diverse and complex visual patterns. The scalability and adaptability of ViTs enable them to handle a wide variety of tasks without the need for task-specific architecture adjustments, unlike CNNs. 

In the following video, we compare noisy videos running both on a CNN-based model and ViT-based model. In every case, ViTs outperform CNN-based models.

Integrating ViTs with TAO Toolkit 5.0

TAO, a low-code AI toolkit to build and accelerate vision AI models, now makes it easy to build and integrate ViTs into your applications and AI workflow. Users can quickly get started with a simple interface and config files to train ViTs, without requiring in-depth knowledge of model architectures. 

The TAO Toolkit 5.0 features several advanced ViTs for popular computer vision tasks including the following.

Fully Attentional Network (FAN)

As a transformer-based family of backbones from NVIDIA Research, FAN achieves SOTA robustness against various corruptions as highlighted in Table 1. This family of backbones can easily generalize to new domains, fighting noise and blur. Table 1 shows the accuracy of all FAN models on the ImageNet-1K dataset for both clean and corrupted versions. 

Global Context Vision Transformer (GC-ViT) 

GC-ViT is a novel architecture from NVIDIA Research that achieves very high accuracy and compute efficiency. It addresses the lack of inductive bias in vision transformers. It also achieves better results on ImageNet with a smaller number of parameters through the use of local self-attention, which combined with global self-attention can give much better local and global spatial interactions. 

Detection transformer with improved denoising anchor (DINO) 

DINO is the newest generation of detection transformers (DETR) with faster training convergence compared to other ViTs and CNNs. DINO in the TAO Toolkit is flexible and can be combined with various backbones from traditional CNNs, such as ResNets, and transformer-based backbones like FAN and GC-ViT. 

Segformer

Segformer is a lightweight and robust transformer-based semantic segmentation. The decoder is made of lightweight multi-head perception layers. It avoids using positional encoding (mostly used by transformers), which makes the inference efficient at different resolutions. 

Powering efficient transformers with NVIDIA L4 GPUs

NVIDIA L4 GPUs are built for the next wave of vision AI workloads. They’re powered by the NVIDIA Ada Lovelace architecture, which is designed to accelerate transformative AI technologies. 

L4 GPUs are suitable for running ViT workloads with their high compute capability of FP8 485 TFLOPs with sparsity. FP8 reduces memory pressure when compared to larger precisions and dramatically accelerates AI throughput. 

The versatility and energy-efficient L4 with single-slot, low-profile form factor makes it ideal for vision AI deployments, including edge locations.

Watch this Metropolis Developer Meetup on-demand to learn more about ViTs, NVIDIA TAO Toolkit 5.0, and L4 GPUs.

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NVIDIA DGX Cloud — which delivers tools that can turn nearly any company into an AI company —  is now broadly available, with thousands of NVIDIA GPUs online on Oracle Cloud Infrastructure, as well as NVIDIA infrastructure located in the U.S. and U.K. Unveiled at NVIDIA’s GTC conference in March, DGX Cloud is an AI Read article >

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Real-time processing of network traffic can be leveraged by the high degree of parallelism GPUs offer. Optimizing packet acquisition or transmission in these…

Real-time processing of network traffic can be leveraged by the high degree of parallelism GPUs offer. Optimizing packet acquisition or transmission in these types of applications avoids bottlenecks and enables the overall execution to keep up with high-speed networks. In this context, DOCA GPUNetIO promotes the GPU as an independent component that can exercise network and compute tasks without the intervention of the CPU.

This post provides a list of GPU packet processing applications focusing on different and unrelated contexts where NVIDIA DOCA GPUNetIO has been integrated to lower latency and maximize performance.

NVIDIA DOCA GPUNetIO API

NVIDIA DOCA GPUNetIO is one of the new libraries released with the NVIDIA DOCA software framework. The DOCA GPUNetIO library enables direct communication between the NIC and the GPU through one or more CUDA kernels. This removes the CPU from the critical path. 

Using the CUDA device functions in the DOCA GPUNetIO library, a CUDA kernel can send and receive packets directly from and to the GPU without the need of CPU cores or memory. Key features of this library include:

• GPUDirect Async Kernel-Initiated Network (GDAKIN): Communications over Ethernet; GPU (CUDA kernel) can directly interact with the network card to send or receive packets in GPU memory (GPUDirect RDMA) without the intervention of the CPU.
• GPU memory exposure: Combine within a single function the basic CUDA memory allocation feature with the GDRCopy library to expose a GPU memory buffer to the direct access (read or write) from the CPU without using CUDA API.
• Accurate Send Scheduling: From the GPU, it’s possible to schedule the transmission of a burst of packets in the future, associate a timestamp to it, and provide this information to the network card, which will in turn take care of sending packets at the right time.
• Semaphores: Useful message passing object to share information and synchronize across different CUDA kernels or between a CUDA kernel and a CPU thread.

For a deep dive into the principles and benefits of DOCA GPUNetIO, see Inline GPU Packet Processing with NVIDIA DOCA GPUNetIO. For more details about DOCA GPUNetIO API, refer to the DOCA GPUNetIO SDK Programming Guide.

Along with the library, the following NVIDIA DOCA application and NVIDIA DOCA sample show how to use functions and features offered by the library. 

• NVIDIA DOCA application: A GPU packet processing application that can detect, manage, filter, and analyze UDP, TCP, and ICMP traffic. The application also implements an HTTP over TCP server. With a simple HTTP client (curl or wget, for example), it’s possible to establish a TCP three-way handshake connection and ask for simple HTML pages through HTTP GET requests to the GPU.
• NVIDIA DOCA sample: GPU send-only example that shows how to use the Accurate Send Scheduling feature (system configuration, functions to use).

DOCA GPUNetIO in real-world applications

DOCA GPUNetIO has been used to empower the NVIDIA Aerial SDK to send and receive using the GPU, getting rid of the CPU. For more details, see Inline GPU Packet Processing with NVIDIA DOCA GPUNetIO. The sections below provide new examples that successfully use DOCA GPUNetIO to leverage the GPU packet acquisition with the GDAKIN technique.

NVIDIA Morpheus AI

NVIDIA Morpheus is a performance-oriented, application framework that enables cybersecurity developers to create fully optimized AI pipelines for filtering, processing, and classifying large volumes of real-time data. The framework abstracts GPU and CPU parallelism and concurrency through an accessible programming model consisting of Python and C++ APIs. 

Leveraging this framework, developers can quickly construct arbitrary data pipelines composed of stages that acquire, mutate, or publish data for a downstream consumer. You can apply Morpheus in different contexts including malware detection, phishing/spear phishing detection, ransomware detection, and many others. Its flexibility and high performance are ideal for real-time network traffic analysis.

For the network monitoring use case, the NVIDIA Morpheus team recently integrated the DOCA framework to implement a high-speed, low-latency GPU packet acquisition source stage to feed real-time packets to an AI-pipeline responsible for analyzing packets’ contents. For more details, visit Morpheus on GitHub.

As shown in Figure 2, GPU packet acquisition happens in real time. Through DOCA Flow, flow steering rules are applied to the Ethernet receive queues, meaning queues can only receive specific types of packets (TCP, for example). Morpheus launches a CUDA kernel, which performs the following steps in a loop:

1. Receive packets using DOCA GPUNetIO receive function
2. Filter and analyze in parallel packets in GPU memory
3. Copy relevant packets info in a list of GPU memory buffers
4. The related DOCA GPUNetIO semaphore item is set to READY when a buffer has accumulated enough packets of information
5. The CUDA kernel in front of the AI pipeline is polling the semaphore item
6. When the item is READY, AI is unblocked as packet information is ready in the buffer

The GTC session Defensive Cyber Operations (DCO) on Edge Networks presents a concrete example in which this architecture is leveraged to deploy a high-performance, AI-enabled SPAN/Network TAP solution. This solution was motivated by the daunting data rates in information technology (IT) and operational technology (OT) networks, heterogeneity of Layer 7 application data, and edge computing size, weight, and power (SWaP) constraints. 

In the case of edge computing, many organizations are unable to “burst to cloud” when the compute demand increases, especially on disconnected edge networks. This scenario requires designing an architecture for I/O and compute challenges that deliver performance across the SWaP spectrum.

This DCO example addresses these constraints through the lens of a common cybersecurity problem, identifying leaked data (leaked passwords, secret keys, and PII, for example) in unencrypted TCP traffic, and represents an extension of the Morpheus SID demo. Identifying and remediating these vulnerabilities reduces the attack surface and increases the security posture of organizations.

In this example, the DCO solution receives packets into a heterogeneous Morpheus pipeline (GPU and concurrent CPU stages written in a mix of Python and C++) that applies a transformer model to detect leaked sensitive data in Layer 7 application data. It integrates outputs with the ELK stack, including an intuitive visualization for Security Operations Center (SOC) analysts to exploit (Figures 3 and 4).

The experimental setup included cloud-native UDP multicast and REST applications running on VMs with a 100 Gbps NVIDIA BlueField-2 DPU. These applications communicated through a SWaP-efficient NVIDIA Spectrum SN2100 Ethernet switch. Packet generators injected sensitive data into packets transmitted by these applications. Network packets were aggregated and mirrored off a SPAN port on the NVIDIA Spectrum SN2100 and sent to an NVIDIA A30X converged accelerator powering the Morpheus packet inspection pipeline achieving impressive throughput results.

• This pipeline includes several components from I/O, packet filtering, packet processing, and indexing in a third-party SIEM platform (Elasticsearch). Focusing only on the I/O aspects, DOCA GPUNetIO enables Morpheus to receive packets into GPU memory at up to 100 Gbps with a single receive queue, removing a critical bottleneck in cyber packet processing applications.
• Leveraging stage level concurrency, the pipeline demonstrated a 60% boost in Elasticsearch indexing throughput.
• Running the end-to-end data pipeline on the NVIDIA A30X converged accelerator generated enriched packets at ~50% capacity of the elasticsearch indexer. Using twice as many A30Xs would fully saturate the indexer, providing a convenient scaling heuristic.

While this use case demonstrates a specific application of Morpheus, it represents the foundational components for cyber packet processing applications. Morpheus plus DOCA GPUNetIO together provide the performance and extensibility for a huge number of latency-sensitive and compute-intensive packet processing applications.

Line-rate radar signal processing

This section walks through an example in which the radar detection application ingests downconverted I/Q samples from a simulated range-only radar system at line rate of 100 Gbps, performing all required signal processing needed to convert the received I/Q RF samples into object detections in real time.

Remote sensing applications such as radar, lidar, and optical platforms rely on signal processing algorithms to turn the raw data collected from the environment they are measuring into actionable information. These algorithms are often highly parallelizable and require a high computational load, making them ideal for GPU-based processing.

Additionally, input sensors generate enormous quantities of raw data, meaning the ingress/egress capability of the processing solution must be able to handle very high bandwidths at low latencies. 

Further complicating the problem, many edge-based sensor systems have strict SWaP constraints, restricting the number and power of available CPU cores that might be used in other high-throughput networking approaches, such as DPDK-based GPUDirect RDMA.

DOCA GPUNetIO enables the GPU to directly handle the networking load as well as the signal processing required to make a real-time sensor streaming application successful.

Commonly used signal processing algorithms were used in the radar detection application. The flowchart in Figure 6 shows a graphical representation of the signal processing pipeline being used to convert the I/Q samples into detections.

MTI filtering is a common technique used to eliminate stationary background clutter, such as the ground or buildings, from the reflected RF waveform in radar systems. The approach used here is known as the Three-Pulse Canceler, which is simply a convolution of the I/Q data in the pulse dimension with the filter coefficients ‘[+1, -2, +1].’

Pulse Compression maximizes the signal-to-noise ratio (SNR) of the received waveform with respect to the presence of targets. It is performed by computing the cross-correlation of the received RF data with the transmitted waveform.

Constant False Alarm Rate (CFAR) detectors compute an empirical estimate of the noise, localized to each range bin of the filtered data. The power of each bin is then compared to the noise and declared a detection if it is statistically likely given the noise estimate and distribution.

A 3D buffer of size (# Waveforms) x (# Channels) x (# Samples) is used to hold the organized RF data being received (note that applying the MTI filter upon packet receipt reduces the size of the pulse dimension to 1). No ordering is assumed to the UDP data streaming in, except that it is streamed roughly in order of the packets’ ascending waveform ID. Around 500 complex samples are transmitted per-packet, and the samples’ location in the 3D buffer is dependent on the waveform ID, channel ID, and sample index.

This application runs two CUDA kernels and one CPU core persistently. The first CUDA kernel is responsible for using the DOCA GPUNetIO API to read packets from the NIC onto the GPU. The second CUDA kernel places packet data into the correct memory location based on metadata in the packets header and applies the MTI filter, and the CPU core is responsible for launching the CUDA kernels that handle Pulse Compression and CFAR. The FFTs were performed using the cuFFT library.

Figure 7 shows a graphical representation of the application.

The throughput of the radar detection pipeline is >100 Gbps. Running at the line rate of 100 Gbps for 1 million 16-channel waveforms, no packets were dropped and the signal processing never fell behind the throughput of the data stream. The latency, measured from when the last data packet for an independent waveform ID was received, was on the order of 3 milliseconds. An NVIDIA ConnectX-6 Dx SmartNIC and an NVIDIA A100 80 GB GPU were used. Data was sent through UDP packets over Ethernet.

Future work will evaluate the performance of this architecture when running exclusively on a BlueField DPU with an integrated GPU.

Real-time DSP services over GPU

Analog signals are everywhere, both artificial (Wi-Fi radio, for example) and natural (solar radiation and earthquakes, for example). To capture analog data digitally, sound waves must be converted using a D-A converter, controlled by parameters such as sample rate and sample bit depth. Digital audio and video can be processed with FFT, allowing sound designers to use tools such as an equalizer (EQ) to alter general characteristics of the signal.

This example explains how NVIDIA products and SDK were used to perform real-time audio DSP with GPU over the network. To do so, the team built a client that parses a WAV file, frames data into multiple Ethernet packets, and sends them over the network to a server application. This application is responsible for receiving packets, applying FFT, manipulating the audio signal, and finally sending back the modified data.

The client’s responsibility is recognizing which portion should be sent to the “server” for the signal processing chain and how to treat the processed samples when they are received back from the server. This approach supports multiple DSP algorithms, such as overlap-add, and various sample window selections.

The server application uses DOCA GPUNetIO to receive packets in GPU memory from a CUDA kernel. When a subset of packets has been received, the CUDA kernel in parallel applies the FFT through the cuFFTDx library to each packet’s payload. In parallel, to each packet, a different CUDA thread applies a frequency filter reducing the amplitude of low or high frequencies. Basically, it applies a low-pass or high-pass filter.

An inverse FFT is applied to each packet. Through DOCA GPUNetIO, the CUDA kernel sends back to the client the modified packets. The client reorders packets and rebuilds them to recreate an audible and reproducible WAV audio file with sound effects applied.

Using the client, the team could tweak parameters to optimize performance and the quality of the audio output. It is possible to separate the flows and multiplex streams into their processing chains, thus offloading many complex computations into the GPU. This is just scratching the potential of this solution, which could open new market opportunities for cloud DSP service providers.

Summary

DOCA GPUNetIO library promotes a generic GPU-centric approach for both packets’ acquisition and transmission in network applications exercising real-time traffic analysis. This post demonstrates how this library can be adopted in a wide range of applications from different contexts, providing huge improvements for latency, throughput, and system resource utilization.

To learn more about GPU packet processing and GPUNetIO, see the following resources:

• Inline GPU Packet Processing with NVIDIA DOCA GPUNetIO
• DOCA GPUNetIO Programming Guide
• DOCA GPUNetIO Application Guide
• Defensive Cyber Security Operations on Edge Networks

 On Aug. 8, Jensen Huang features new NVIDIA technologies and award-winning research for content creation.

 On Aug. 8, Jensen Huang features new NVIDIA technologies and award-winning research for content creation.

Modeling time series data can be challenging (and fascinating) due to its inherent complexity and unpredictability. Long-term trends in time series can change…

Modeling time series data can be challenging (and fascinating) due to its inherent complexity and unpredictability. Long-term trends in time series can change drastically due to certain events, for example. Recall the beginning of the global pandemic, when businesses such as airlines or brick-and-mortar shops saw a quick decline in the number of customers and sales. In contrast, e-commerce businesses continued to operate with less disruption.

Interaction terms can help model such patterns. They capture complex relationships between variables and, as a result, lead to more accurate predictions.

This post explores:

• Interaction terms in the context of time series forecasting
• Benefits of interaction terms when modeling complex relationships
• How to effectively implement interaction terms in your models 

Overview of interaction terms

Interaction terms enable you to investigate whether the relationship between the target and a feature changes depending on the value of another feature. For more details, see my previous post, A Comprehensive Guide to Interaction Terms in Linear Regression.

Figure 1 shows a scatterplot that represents the relationship between miles per gallon (target) and the weight of a vehicle (feature). The relationship is quite different depending on the transmission type (another feature).

Improving linear model accuracy

Without using interaction terms, a linear model would not be able to capture such a complex relationship. Effectively, it would assign the same coefficient for the weight feature, regardless of the type of transmission. Figure 1 shows the coefficients (slope of the line) by weight feature, which are drastically different for different transmission types.

To overcome this fallacy and make the linear model more flexible, add interaction terms. In general, they are a multiplication of the original features. By adding these new variables to the regression model, you can measure the effects of the interaction between them and the target. 

Interaction terms in time series forecasting

Interaction terms make linear models more flexible. The following example shows how they work in the context of time series forecasting.

Prerequisites

First, load the required libraries:

import numpy as np
import pandas as pd

from sklearn.linear_model import LinearRegression

import seaborn as sns
import matplotlib.pyplot as plt

Dataset generation

Then, generate some artificial time series data with the following characteristics:

• 10 years of daily data
• Repeating patterns (seasonality) present in the time series
• A decreasing trend over the first 7 years
• No trend in the last 3 years
• Random noise, added as the last step

# for reproducibility
np.random.seed(42)

# generate the DataFrame with dates
range_of_dates = pd.date_range(
   start="2010-01-01",
   end="2019-12-30"
)
df = pd.DataFrame(index=range_of_dates)

# create a sequence of day numbers
df["linear_trend"] = range(len(df))
df["trend"] = 0.004 * df["linear_trend"].values[::-1]
df.loc["2017-01-01":, "trend"] = 4

# generate the components of the target
signal_1 = 10 + 4 * np.sin(df["linear_trend"] / 365 * 2 * np.pi)
noise = np.random.normal(0, 0.85, len(df))

# combine them to get the target series
df["target"] = signal_1 + noise + df["trend"]

# plot
df["target"].plot(title="Generated time series");

Figure 2 shows the generated time series, which includes all the desired characteristics.

Training the benchmark model

Now train a linear model and inspect the best fit line. For this step, create very simple models with a few features. This enables you to visually inspect the impact of the interaction term on the model’s fit.

The simplest model possible contains one feature — an indicator of the passage of time. The linear_trend column created for the time series is effectively the row number of the DataFrame (ordered by date).

X = df[["linear_trend"]]
y = df[["target"]]

lm = LinearRegression()
lm.fit(X, y)

df["model_1"] = lm.predict(X)

df[["target", "model_1"]].plot(title="Linear trend");

It is worth mentioning that the point is not to properly evaluate the forecasts using separate train and test sets, but rather to explain the impact of the interaction terms on the model’s fit. It is easier to observe the interaction term’s impact by inspecting the fitted values (prediction on the training set) and comparing those fitted values to the original time series. 

Figure 3 shows that the linear model identified a decreasing trend for the entire time series. At the same time, the fit seems off for the last 3 years of data, as there is no trend there.

Add a breakpoint

Next, try to make the model learn the new pattern (trend change) using feature engineering. To do so, create a breakpoint, which is a placeholder variable indicating whether a given observation is after January 1, 2017. In this case, the exact point in time when the trend change happened is known. 

Next, train another linear model, this time with two features:

df["after_2017_breakpoint"] = np.where(df.index >= pd.Timestamp('2017-01-01'), 1, 0)

X = df[["linear_trend", "after_2017_breakpoint"]]
y = df[["target"]]

lm = LinearRegression()
lm.fit(X, y)

df["model_2"] = lm.predict(X)

df[["target", "model_2"]].plot(title="Linear trend + breakpoint");

Figure 4 shows a few important changes, as listed below:

• The fitted line displays a vertical jump, which corresponds to the coefficient by the new Boolean feature.
• The vertical jump occurs exactly on the first date when the feature becomes active (a value of 1 instead of 0).
• The slope of the line is the same before and after the introduced breakpoint.
• The model is trying to compensate for the incorrect slope by adding a fixed amount to the predictions after the breakpoint.

There is no trend in the last 3 years of data, so ideally the line should be close to flat after January 1, 2017. 

Adding an interaction term

To change the slope after the breakpoint, add a more complex dependency on the timestamp (represented by a linear trend). That is exactly what an interaction term does–it is a multiplication of the linear trend and the placeholder variable.

df["interaction_term"] = df["after_2017_breakpoint"] * df["linear_trend"]

X = df[["linear_trend", "after_2017_breakpoint", "interaction_term"]]
y = df[["target"]]

lm = LinearRegression()
lm.fit(X, y)

df["model_3"] = lm.predict(X)

df[["target", "model_3"]].plot(title="Linear trend + breakpoint + interaction term");

Figure 5 shows the impact of having the interaction term in the model. Compared to Figure 4, the slope of the best fit line is different after the breakpoint. 

To be more precise, the difference is actually the value of the coefficient by the interaction term. While the new line did not flatten out, it is still less steep than it used to be in the earlier parts of the time series.

Introducing the breakpoint together with an interaction term increased the model’s ability to capture the trend of the time series. In turn, that should increase the predictive performance of the model.

Summary

Using interaction terms can make the specification of a linear model more flexible (different slopes for different lines), which can result in a better fit to the data and better predictive performance. You can add interaction terms as a multiplication of the original features. In the context of time series, you can use interaction terms to better capture any changes to the trend.

Find the code used in this post in A Comprehensive Guide on Interaction Terms in Time Series Forecasting on GitHub. Additionally, the code in the notebook shows how to leverage cuDF and cuML to train your models using GPU acceleration. As always, feedback is welcome. You can reach out to me on Twitter or in the comments.