Where to cut: Efficient ADC quantization for analog in-memory computing with discrete values

Johannes Leugering, Shashank Bansal, Zhaoyi Liu, Jiahao Song, Bouchaib Cherif, Gert Cauwenberghs

Abstract: Many proposed in-memory-computing systems use analog memristive crossbars to compute matrix-vector products over discrete domains. This yields analog outputs distributed around discrete values across a wide nominal range. Lossless quantization of this range requires costly high-precision analog-to-digital converters (ADCs), which limits the applicability of this approach. But typical results are highly concentrated in a small central region; hence, an ADC with lower resolution that only operates in this central region can achieve almost full accuracy at a fraction of the cost. In this paper, we explore how to appropriately choose ADC resolution and the covered region of interest, specifically for low-precision applications in approximate in-memory-computing. Our results reveal two distinct strategies: ADCs with sufficient resolution should (at least) capture the region of interest without loss, whereas lower-resolution ADCs should space their levels just enough to cover the region of interest. We argue that using this scheme could drastically improve power efficiency and thus scalability of compute-in-memory architectures.

External IDs:dblp:conf/iscas/LeugeringBLSCC25