In many dynamic and evolving markets, such as 5G cellular, data center, automotive, and industrial, applications are pushing for ever increasing compute acceleration while remaining power efficient. With Moore's Law and Dennard Scaling no longer following their traditional trajectories, moving to the next-generation silicon node alone cannot deliver the benefits of lower power and cost with better performance as with previous generations.
Responding to this non-linear increase in demand by next-generation applications, like wireless beamforming and machine learning inference, AMD has developed an innovative processing technology, the AI Engine, as part of the AMD Versal™ architecture.
AI Engines are architected as 2D arrays consisting of multiple AI Engine tiles and allow for a very scalable solution across the Versal portfolio, ranging from 10s to 100s of AI Engines in a single device, servicing the compute needs of a breadth of applications. Benefits include:
For high-performance DSP applications, the following methods are available for coding AI Engines (for more information please visit: AMD Vitis™ AI Engine DSP Design)
Each AI Engine tile consists of a very long instruction word (VLIW), single instruction multiple data (SIMD) vector processor optimized for machine learning and advanced signal processing applications. The AI Engine processor can run up to 1.3 GHz, enabling very efficient, high-throughput and low-latency functions.
As well as the VLIW vector processor, each tile contains program memory to store the necessary instructions; local data memory for storing data, weights, activations and coefficients; and a RISC scalar processor and different modes of interconnect to handle different types of data communication.
AMD offers two types of AI Engines: AIE and AIE-ML (AI Engine for machine learning), both offering significant performance improvements over previous generation FPGAs. AIE accelerates a more balanced set of workloads including ML inference applications and high-performance DSP signal processing workloads like beamforming, radar, and other workloads requiring a massive amount of filtering and transforms. With enhanced AI vector extensions and the introduction of shared memory tiles within the AI Engine array, AIE-ML offers superior performance over AIE for ML inference-focused applications, while AIE can offer better performance over AIE-ML for certain types of advanced signal processing.
AIE accelerates a balanced set of workloads, including ML inference applications and advanced signal processing workloads like beamforming, radar, FFTs, and filters.
See AMD Versal AI Engine Architecture Manual to learn more.
*BFLOAT16 implemented using FP32 vector processor.
The AI Engine-ML architecture is optimized for machine learning, enhancing both the compute core and memory architecture. Capable of both ML and advanced signal processing, these optimized tiles de-emphasize INT32 and CINT32 support (common in radar processing) to enhance ML-focused applications.
AIE-ML will be available in two versions: AIE-ML, which doubles the compute compared to AIE, and AIE-MLv2, which doubles the compute compared to AIE-ML and adds extra bandwidth between the stream interconnects.
**SW emulation for AIE-ML FP32 support.
The AI Engine, along with programmable logic and a processing system, form a tightly integrated heterogeneous architecture in Versal adaptive SoCs that can be changed at both the hardware and software levels to dynamically adapt to the needs of a wide range of applications and workloads.
Built from the ground up to be natively software programmable, the Versal architecture features a flexible, multi-terabit per-second programmable network on chip (NoC) to seamlessly integrate all components and key interfaces, making the platform available at boot and easily programmed by software developers, data scientists, and hardware developers alike.
AI Engines for Heterogeneous Workloads—Ranging from Wireless Processing to Machine Learning in the Cloud, Network, and Edge
Data Center Compute
Image and video analysis is central to the explosion of data in the data center. The convolutional neural network (CNN) nature of these workloads requires intense amounts of computation – often reaching multiple teraOPS. AI Engines have been optimized to deliver this computational density cost effectively and power efficiently.
5G Wireless Processing
5G can provide unprecedented throughput at extremely low latency, necessitating a significant increase in signal processing. AI Engines can execute this real-time signal processing in the radio unit (RU) and distributed unit (DU) at lower power, such as with the sophisticated beamforming techniques used in massive MIMO panels to increase network capacity.
ADAS and Automated Drive
CNNs are a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. CNNs have become essential as computers are used for everything from autonomous driving vehicles to video surveillance. AI Engines provide the necessary compute density and efficiency required for small form factors with tight thermal envelopes.
Aerospace & Defense
Merging powerful vector-based DSP Engines with AI Engines in a small form factor enables a breadth of systems in A&D, including phased array radar, early warning (EW), MILCOM, and unmanned vehicles. Supporting heterogeneous workloads ranging from signal processing, signal conditioning, and AI inference for multi-mission payloads, AI Engines deliver the compute efficiency to meet the aggressive size, weight, and power (SWaP) requirements of these mission-critical systems.
Industrial
Industrial applications, including robotics and machine vision, combine sensor fusion with AI/ML to perform data processing at the edge and near the source of information. AI Engines improve performance and dependability in these real-time systems, despite the uncertainty of the environment.
Wireless Test Equipment
Real-time DSP is used extensively in wireless communications test equipment. The AI Engine architecture is well-suited to handle all types of protocol implementations, including 5G from the digital front-end to beamforming and baseband.
Healthcare
Healthcare applications leveraging AI Engines include high-performance parallel beamformers for medical ultrasound, back projection for CT scanners, offloading of image reconstruction in MRI machines, and assisted diagnosis in a variety of clinical and diagnostic applications.
AI Engines are built from the ground up to be software programmable and hardware adaptable. There are two distinct design flows for developers to unleash the performance of these compute engines with the ability to compile in minutes and rapidly explore different microarchitectures. The two design flows consist of:
AI Engine arrays can also enable the implementation of high-performance DSP functions in a resource- and power-optimized manner. Use of AI Engines in conjunction with the FPGA fabric resources can enable very efficient implementation of high-performance DSP applications. Learn how to use the AMD Vitis tool flow to unlock the hardware acceleration capabilities of AI Engines for DSP applications: AMD Vitis AI Engine DSP Design
With the Vitis Acceleration library, AMD provides pre-built kernels that enable:
Software and hardware developers directly program the vector processor-based AI Engines and can call on pre-built libraries with C/C++ code where appropriate.
AI data scientists stay in their familiar framework environments, such as PyTorch or TensorFlow, and call pre-built ML overlays by way of Vitis AI without having to directly program the AI Engines.
The libraries are open source and available on GitHub: https://github.com/Xilinx/Vitis_Libraries.
The AI Engine architecture is based on a data flow technology. Processing elements come in arrays of 10 to 100 tiles–creating a single program across compute units. For a designer to embed directives to specify parallelism across these tiles would be tedious and nearly impossible. To overcome this difficulty, AI Engine design is performed in two stages: single kernel development followed by Adaptive Data Flow (ADF) graph creation, which connects multiple kernels into an overall application.
Vitis Unified IDE provides a single IDE cockpit that enables AI Engine kernel development using C/C++ programming code and ADF graph design. Specifically, designers can:
A single kernel runs on a single AI Engine tile by default. However, multiple kernels can run on the same AI Engine tile, sharing the processing time where the application allows.
A conceptual example is shown below:
Within the Vitis Unified IDE, the AI Engine design can be included into a larger complete system that combines all aspects of the design into an integrated flow where simulation, hardware emulation, debug, and deployment are possible.
The VEK280 Evaluation Kit, equipped with the Versal AI Edge VE2802 adaptive SoC, offers AIE-ML and DSP hardware acceleration engines, along with multiple high-speed connectivity options.. This kit is optimized for ML inference applications in markets such as automotive, vision, aerospace and defense, industrial, scientific, and medical.
The VCK190 Evaluation Kit enables designers to develop solutions using AI and DSP Engines capable of delivering over 100X greater compute performance compared to current server class CPUs. With a breadth of connectivity options and standardized development flows, the Versal AI Core Series VC1902 device provides the Versal portfolio's highest AI inference and signal processing throughput for cloud, network, and edge applications.
The AMD Vitis unified software platform provides comprehensive core development kits and libraries that use hardware-acceleration technology.
Visit the Vitis GitHub and AI Engine Development repositories to access a variety of AI Engine tutorials and learn more about the technology features and design methodology.
AI Engine tools, both compiler and simulator, are integrated within the Vitis IDE and require an additional dedicated license. Contact your local AMD sales representative for more information on how to access the AI Engine tools and license or visit the Contact Sales form.
AMD Vitis Model Composer is a model-based design tool that enables rapid design exploration within the Simulink® and MATLAB® environments. It facilitates AI Engine ADF graph development and testing at the system level, allowing users to incorporate RTL and HLS blocks with AI Engine kernels and/or graphs in the same simulation. Leveraging the signal generation and visualization features within the Simulink and MATLAB tool enables DSP engineers to design and debug in a familiar environment. To learn how to use Versal AI Engines with Vitis Model Composer, visit the AI Engine resource page.
Based on the Versal AI Core Series, the VCK190 kit enables designers to develop solutions using AI Engines and DSP Engines. The evaluation kit has everything you need to jump-start your designs.
Also available is the PCIe®-based VCK5000 development card, featuring the Versal AI Core device with AI Engines, built for high-throughput AI inference in the data center.
For AIE-ML development, the VEK280 Evaluation Kit, based on the Versal AI Edge Series, will enable developers for DSP and ML applications.
AMD training and learning resources provide the practical skills and fundamental knowledge you need to be fully productive in your next Versal adaptive SoC development project. Courses include:
From solution planning to system integration and validation, AMD provides tailored views of the extensive list of Versal adaptive SoC documentation to maximize the productivity of user designs. Visit the Versal adaptive SoC design process hubs to get the latest content for your design needs and explore AI Engine capabilities and design methodologies.
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