Multiple-base Logarithmic Quantization and Application in Reduced Precision AI Computations Proceedings Paper

Dimitrov, V, Ford, R, Imbert, L et al. (2024). Multiple-base Logarithmic Quantization and Application in Reduced Precision AI Computations . 14 63-70. 10.55630/dipp.2024.14.5

cited authors

  • Dimitrov, V; Ford, R; Imbert, L; Madanayake, A; Udayanga, N; Wray, W

abstract

  • The power of logarithmic quantizations and computations has been recognized as a useful tool in optimizing the performance of large ML models. There are plenty of applications of ML techniques in digital preservation. The accuracy of computations may play a crucial role in the corresponding algorithms. In this article, we provide results that demonstrate significantly better quantization signal-to-noise ratio performance thanks to multiple-base logarithmic number systems (MDLNS) in comparison with the floating point quantizations that use the same number of bits. On a hardware level, we present details about our Xilinx VCU-128 FPGA design for dot product and matrix vector computations. The MDLNS matrix-vector design significantly outperforms equivalent fixed-point binary designs in terms of area (A) and time (T) complexity and power consumption as evidenced by a 4 × scaling of AT2 metric for VLSI performance, and 57% increase in computational throughput per watt compared to fixed-point arithmetic.

publication date

  • January 1, 2024

Digital Object Identifier (DOI)

start page

  • 63

end page

  • 70

volume

  • 14