  A mass of data transfer between the processing and storage units has been the
leading bottleneck in modern Von-Neuman computing systems, especially when used
for Artificial Intelligence (AI) tasks. Computing-in-Memory (CIM) has shown
great potential to reduce both latency and power consumption. However, the
conventional analog CIM schemes are suffering from reliability issues, which
may significantly degenerate the accuracy of the computation. Recently, CIM
schemes with digitized input data and weights have been proposed for high
reliable computing. However, the properties of the digital memory and input
data are not fully utilized. This paper presents a novel low power CIM scheme
to further reduce the power consumption by using a Modified Radix-4 (M-RD4)
booth algorithm at the input and a Modified Canonical Signed Digit (M-CSD) for
the network weights. The simulation results show that M-Rd4 and M-CSD reduce
the ratio of $1\times1$ by 78.5\% on LeNet and 80.2\% on AlexNet, and improve
the computing efficiency by 41.6\% in average. The computing-power rate at the
fixed-point 8-bit is 60.68 TOPS/s/W.
