Efficient Non-linear Calculators
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Abstract (arXiv)
A novel algorithm for producing smooth nonlinearities on digital hardware is presented. The non-linearities are inherently quadratic and have both symmetrical and asymmetrical variants. The integer (and fixed point) implementation is highly amenable for use with digital gates on an ASIC or FPGA. The implementations are multiplier-less. Scaling of the non-linear output, as required in an LSTM cell, is integrated into the implementation. This too does not require a multiplier. The non-linearities are useful as activation functions in a variety of ANN architectures. The floating point mappings have been compared with other non-linearities and have been benchmarked. Results show that these functions should be considered in the ANN design phase. The hardware resource usage of the implementations have been thoroughly investigated. Our results make a strong case for implementions in edge applications. This document summarizes the findings and serves to give a quick overview of the outcomes of our researchfootnoteThe authors peer-reviewed manuscripts (available at https://doi.org/10.1016/j.neucom.2021.02.030) offer more detail and may be better suited for a thorough consideration.
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References · 4
- 2016ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGAcited by 50
- 2018C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAscited by 21
- 2018FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAscited by 10
- 2018Design Space Exploration of Neural Network Activation Function Circuitscited by 4
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