  Today, almost all computer systems use IEEE-754 floating point to represent
real numbers. Recently, posit was proposed as an alternative to IEEE-754
floating point as it has better accuracy and a larger dynamic range. The
configurable nature of posit, with varying number of regime and exponent bits,
has acted as a deterrent to its adoption. To overcome this shortcoming, we
propose fixed-posit representation where the number of regime and exponent bits
are fixed, and present the design of a fixed-posit multiplier. We evaluate the
fixed-posit multiplier on error-resilient applications of AxBench and OpenBLAS
benchmarks as well as neural networks. The proposed fixed-posit multiplier has
47%, 38.5%, 22% savings for power, area and delay respectively when compared to
posit multipliers and up to 70%, 66%, 26% savings in power, area and delay
respectively when compared to 32-bit IEEE-754 multiplier. These savings are
accompanied with minimal output quality loss (1.2% average relative error)
across OpenBLAS and AxBench workloads. Further, for neural networks like
ResNet-18 on ImageNet we observe a negligible accuracy loss (0.12%) on using
the fixed-posit multiplier.
