  Growing uncertainty in design parameters (and therefore, in design
functionality) renders stochastic computing particularly promising, which
represents and processes data as quantized probabilities. However, due to the
difference in data representation, integrating conventional memory (designed
and optimized for non-stochastic computing) in stochastic computing systems
inevitably incurs a significant data conversion overhead. Barely any stochastic
computing proposal to-date covers the memory impact. In this paper, as the
first study of its kind to the best of our knowledge, we rethink the memory
system design for stochastic computing. The result is a seamless stochastic
system, StochMem, which features analog memory to trade the energy and area
overhead of data conversion for computation accuracy. In this manner StochMem
can reduce the energy (area) overhead by up-to 52.8% (93.7%) at the cost of at
most 0.7% loss in computation accuracy.
