A User-Friendly Ecosystem for AI FPGA-Based Accelerators

The introduction of FPGAs in High-Performance Embedded Computing and Artificial Intelligence still faces chal-enges regarding the difficulty of getting started. It requires hardware knowledge, familiarity with multiple tooling, libraries and frameworks and long synthesis times. To encourage the usage of FPGAs, this work proposes an ecosystem that includes a library with a set of pre-built accelerators for common Digital Signal Processing and Artificial Intelligence workloads, an engine for runtime arbitrary-precision quantisation and an agnostic API, allowing the development of FPGA-accelerated user applications while abstracting the details about the FPGA design and implementation. Our approach is based on hardware reuse, introducing software resource management of a series of pre-built IP cores, allowing low-end FPGAs to be used as hardware accelerators and multiple applications to share resources. Our work is better than managed FPGA standalone applications with Vitis HLS-based quantisation, accelerating 1.22 x, thanks to our quantisation engine, which accelerates 5.12 x the quantisation and 13.30 x the de-quantisation, while keeping close the accelerator execution times.