FPGA Implementation of N-BEATS for Time Series Forecasting Using Block Minifloat Arithmetic

Abstract

The block minifloat (BM) number format uses an 8-bit floating point format with additional shared exponent bias to enable low-precision representation with large dynamic range. While it has been shown that the BM format can support low-precision training of convolutional neural networks such as ResNet on ImageNet at precisions down to 6 bits, its applicability to inference-only applications has not been studied. We present a BM implementation of N-BEATS, a deep neural architecture for univariate time series forecasting. N-BEATS utilises residual and fully connected (FC) blocks to achieve high accuracy. It was found that 8-bit BM had similar area and speed as 8-bit integer arithmetic with NBEATS accuracy similar to 16-bit floating point.