EFFICIENT INFERENCE OF IMAGE-BASED NEURAL NETWORK MODELS IN RECONFIGURABLE SYSTEMS WITH PRUNING AND QUANTIZATION

Neural networks (NN) for image processing in embedded systems expose two conflicting requirements: increasing computing power needs as models become more complex and constrained resource budget. In order to alleviate this problems, model compression based on quantization and pruning techniques are common. Derived models then need to fit on reconfigurable systems such as FPGAs for the embedded system to work properly. In this paper, we present HLSinf, an open source framework for the development of custom NN accelerators for FPGAs which provides efficient support to quantized and pruned NN models. With HLSinf, significant inference speedups can be obtained for typical medical image-based applications. In particular, we obtain up to 90x speedup factor when we combine quantization/pruning with the flexibility of HLSinf compared to CPU.