Wednesday, February 12, 2025

FPGA AI Suite Latest Developments For Peak Performance

FPGA AI Suite

Version 2024.3, the most recent edition of the FPGA AI Suite software, is jam-packed with improvements that improve your development experience. New features that tackle the difficulties developers have in the real world are included in this update. Every enhancement is intended to make your work more productive and efficient, from improving compiler tools to increasing performance to providing fresh design examples.

The enhanced performance scaling with Multilane is among the most important developments in FPGA AI Suite 2024.3. The new “num_lane” architecture greatly improves computing performance by processing many rows of a tensor at once. You are scaling the number of channels that the IP processes rather than the number of IP instances. For DDR-free designs designs that do not require the weights of a model to be stored in external memory this leads to a noticeably higher throughput. In order to facilitate quicker and more effective processing, Intel allow up to four lanes. Intel has seen performance gains of up to 2.7x in real-world designs.

FPGA AI Suite 2024.3’s new example designs are made to make your development process go more smoothly. An effective and small Artificial Intelligence solution for models that can be fully constructed within the FPGA fabric is demonstrated by the Agilex 5 FPGA E-Series hostless example design, which is based on Altera’s Premium Development Kit. This configuration avoids the need for external DDR memory by storing input and output AXI-Stream interfaces in FPGA on-chip memory.

Inference can be done entirely in on-chip memory because weights and instructions are compiled straight into the FPGA bitstream. If the filter cache or stream buffer is too tiny for the graph, the compiler will generate errors and make sure that data does not overflow into external memory. By doing away with the requirement for off-chip memory buffer management, this DDR-free architecture streamlines operations, decreases power consumption, and lowers system costs. Furthermore, the Agilex 7 FPGA PCIe-attach Open FPGA Stack-based designs offer production-quality, fully tested IP that can be managed by System Console and JTAG, providing useful, ready-to-use solutions for effective project development.

The FPGA AI Suite’s compiler enhancements are intended to provide you with more control and understanding over your designs. Estimating power usage is made easier by the Quartus Prime Power and Thermal Calculator’s automatic file production. To assist you understand which layers are operating on FPGA fabric versus the CPU, other improvements to the Model Analyser include more thorough information in graph files and extensive reports on auxiliary modules. With the help of these improvements, you may maximise performance and efficiency by making well-informed judgements regarding your designs.

A number of improvements offered by FPGA AI Suite 2024.3 can have a big influence on your development process. This release tackles the issues that developers frequently encounter and provides solutions that might improve your projects by emphasising performance, offering useful design examples, and improving compiler tools. As Intel work to make FPGA AI Suite the greatest tool for your needs, Intel is eager to learn how you will use these new capabilities and welcome your ongoing comments.

FPGA AI Suite Features

Superior Performance

The Agilex 7 FPGA M-Series, for example, can process up to 3,679 ResNet-50 frames per second at 90% FPGA utilisation or theoretically reach a maximum speed of 88.5 INT8 TOPS.

AI Front-End Assistance

The suite offers versatility in model creation by supporting many Artificial Intelligence front ends, such as TensorFlow, Caffe, PyTorch, MXNet, Keras, and ONNX.

Seamless Pre-Trained Model Conversion

Models from conventional frameworks can be transformed into intermediate representations using the OpenVINO toolkit, making it easier to incorporate them into FPGA designs.

Optimized AI IP Generation

The suite balances resource usage and performance goals to produce the best AI inference IP from pre-trained models.

Hardware-less Early Model Validation

Through the OpenVINO plugin interface, bit-accurate software emulation of the AI inference IP is accessible, allowing for a faster assessment of model accuracy without requiring hardware.

Drakshi
Drakshi
Since June 2023, Drakshi has been writing articles of Artificial Intelligence for govindhtech. She was a postgraduate in business administration. She was an enthusiast of Artificial Intelligence.
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