  Computation and Data Reuse is critical for the resource-limited Convolutional
Neural Network (CNN) accelerators. This paper presents Universal Computation
Reuse to exploit weight sparsity, repetition, and similarity simultaneously in
a convolutional layer. Moreover, CoDR decreases the cost of weight memory
access by proposing a customized Run-Length Encoding scheme and the number of
memory accesses to the intermediate results by introducing an input and output
stationary dataflow. Compared to two recent compressed CNN accelerators with
the same area of 2.85 mm^2, CoDR decreases SRAM access by 5.08x and 7.99x, and
consumes 3.76x and 6.84x less energy.
