%AYu, S.%ATan, S.%D2022%I
%K
%MOSTI ID: 10393030
%PMedium: X
%TScaled-CBSC: Scaled counting-based stochastic computing multiplication for improved accuracy
%XStochastic computing (SC) can lead area-efficient implementation of
logic designs. Existing SC multiplication, however, suffers a
long-standing problem: large multiplication error with small
inputs due to its intrinsic nature of bit-stream based computing.
In this article, we propose a new scaled counting-based SC
multiplication approach, called {\it Scaled-CBSC}, to mitigate this
issue by introducing scaling bits to ensure the bit `1' density of
the stochastic number is sufficiently large. The idea is to convert
the ``small'' inputs to ``large'' inputs, thus improve the accuracy
of SC multiplication. But different from an existing stream-bit
based approach, the new method uses the binary format and does not
require stochastic addition as the SC multiplication always starts
with binary numbers. Furthermore, Scaled-CBSC only requires all the
numbers to be larger than 0.5 instead of arbitrary defined
threshold, which leads to integer numbers only for the scaling term.
The experimental results show that the 8-bit Scaled-CBSC
multiplication with 3 scaling bits can achieve up to 46.6\% and
30.4\% improvements in mean error and standard deviation,
respectively; reduce the peak relative error from 100\% to 1.8\%;
and improve 12.6\%, 51.5\%, 57.6\%, 58.4\% in delay, area,
area-delay product, energy consumption, respectively, over the state
of art work.
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