Microchip Reveals Software Development Kit and Neural Network IP for Easily Creating Low-Power FPGA Smart Embedded Vision Solutions

Microchip’s VectorBlox SDK and IP offers an easy way for software developers to program a trained neural network without prior FPGA expertise

CHANDLER, Ariz., May 18, 2020 (GLOBE NEWSWIRE) -- With the rise of Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT), applications are moving to the network edge where data is collected, requiring power-efficient solutions to deliver more computational performance in ever smaller, thermally constrained form factors. Through its Smart Embedded Vision initiative, Microchip Technology Inc. (Nasdaq: MCHP) is meeting the growing need for power-efficient inferencing in edge applications by making it easier for software developers to implement their algorithms in PolarFire field-programmable gate arrays (FPGAs). As a significant addition to the solutions portfolio in this segment, Microchip’s VectorBlox Accelerator Software Development Kit (SDK) helps developers take advantage of Microchip’s PolarFire FPGAs for creating low-power, flexible overlay-based neural network applications without learning an FPGA tool flow.  

FPGAs are ideal for edge AI applications, such as inferencing in power-constrained compute environments, because they can perform more giga operations per second (GOPS) with greater power efficiency than a central processing unit (CPU) or graphics processing unit (GPU), but they require specialized hardware design skills. Microchip’s VectorBlox Accelerator SDK is designed to enable developers to code in C/C++ and program power-efficient neural networks without prior FPGA design experience.

The highly flexible tool kit can execute models in TensorFlow and the open neural network exchange (ONNX) format which offers the widest framework interoperability. ONNX supports many frameworks such as Caffe2, MXNet, PyTorch, and MATLAB. Unlike alternative FPGA solutions, Microchip’s VectorBlox Accelerator SDK is supported on Linux and Windows operating systems, and it also includes a bit accurate simulator which provides the user the opportunity to validate the accuracy of the hardware while in the software environment. The neural network IP included with the kit also supports the ability to load different network models at run time.