ResourceEfficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs FPGAs have been successfully used for the implementation of dedicated accelerators for a wide range of machine learning problems. The inference in socalled SumProduct Networks can also be accelerated efficiently using a pipelined FPGA architecture. However, as SumProduct Networks compute exact probability values, the required arithmetic precision poses different challenges than those encountered with Neural Networks. In previous work, this precision was maintained by using doubleprecision floatingpoint number formats, which are expensive to implement in FPGAs. In this work, we propose the use of a logarithmic number system format tailored specifically towards the inference in SumProduct Networks. The evaluation of our optimized arithmetic hardware operators shows that the use of logarithmic number formats allows to save up to 50% hardware resources compared to doubleprecision floating point, while maintaining sufficient precision for SPN inference at almost identical performance.

Static Block FloatingPoint Quantization for Convolutional Neural Networks on FPGA Convolutional neural networks (CNNs) have been widely applied in various computer vision and speech processing applications. However, the algorithmic complexity of CNNs hinders their deployment in embedded systems with limited memory and computational resources. This paper proposes static block floatingpoint (BFP) quantization, an effective approach involving KullbackLeibler divergence, to determine the static shared exponents. Without need for retraining, the proposed approach is able to quantize CNNs to 8 bits with negligible accuracy loss. An FPGAbased hardware design with static BFP quantization is also proposed. Compared with 8bit integer linear quantization, our experiments show that the hardware kernel based on static BFP quantization can achieve over 50% reduction in logic resources on an FPGA. Based on static BFP quantization, a tool implemented in the PyTorch framework is developed, which can automatically generate optimised configuration according to user requirements for given CNN models, where the entire optimization process takes only a few minutes on an Intel Xeon Silver 4110 CPU.