In-memory computing (IMC) offloads parts of the computations to memory to fulfill the performance and energy demands of applications such as neuromorphic computing, machine learning, and image processing. Fortunately, the main features that stochastic computing (SC) and IMC share, which are low computation complexity and high bit-parallel computation capability, promise great potential for integrating SC and IMC. In this paper, we exploit this potential by using stochastic computation as an approximation method to present effective in-memory computations with a good trade-off among design parameters. To this end, first, commonly used stochastic arithmetic operations of applications are effectively implemented using the primitive logic gates of the IMC method. Next, the in-memory scheduling and mapping of applications are obtained efficiently by a proposed algorithm. This algorithm reduces the computation latency by enabling intra-subarray parallelism while considering the IMC method constraints. Subsequently, a bit-parallel stochastic IMC architecture, Stoch-IMC, is presented that enables bit parallelization of stochastic computations over memory subarrays/banks. To evaluate Stoch-IMC's effectiveness, various analyses were conducted. Results show average performance improvements of 135.7X and 124.2X across applications compared to binary IMC and related in-memory SC methods, respectively. The results also demonstrate an average energy reduction of 1.5X compared to binary IMC, with limited energy overhead relative to the in-memory SC method. Furthermore, the results reveal average lifetime improvements of 4.9X and 216.3X over binary IMC and in-memory SC methods, respectively, along with high bitflip tolerance.

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