ONNX-to-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs

Neural Networks (NNs) provide a solid and reliable way of executing different types of applications, ranging from speech recognition to medical diagnosis, speeding up onerous and long workloads. The challenges involved in their implementation at the edge include providing diversity, flexibility, and sustainability. That implies, for instance, supporting evolving applications and algorithms energy-efficiently. Using hardware (hw) or software accelerators can deliver fast and efficient computation of the NNs, while flexibility can be exploited to support long-term adaptivity. Nonetheless, handcrafting an NN for a specific device,
despite the possibility of leading to an optimal solution, takes time and experience, and that’s why frameworks for hw accelerators are being developed. This work, starting from a preliminary semi-integrated ONNX-to-hardware toolchain [21], focuses on enabling Approximate Computing (AC) leveraging the distinctive ability of the original toolchain to favor adaptivity. The goal is to allow lightweight adaptable NN inference on FPGAs at the edge.