Characterizing and Optimizing Realistic Workloads on a Commercial Compute-in-SRAM Device

Compute-in-SRAM architectures offer a promising approach to achieving higher performance and energy efficiency across a range of data-intensive applications. However, prior evaluations have largely relied on simulators or small prototypes, limiting the understanding of their real-world potential. In this work, we present a comprehensive performance and energy characterization of a commercial compute-in-SRAM device, the GSI APU, under realistic workloads. We compare the GSI APU against established architectures, including CPUs and GPUs, to quantify its energy efficiency and performance potential. We introduce an analytical framework for general-purpose compute-in-SRAM devices that reveals fundamental optimization principles by modeling performance trade-offs, thereby guiding program optimizations.

Exploiting the fine-grained parallelism of tightly integrated memorycompute architectures requires careful data management. We address this by proposing three optimizations: communication-aware reduction mapping, coalesced DMA, and broadcast-friendly data layouts. When applied to retrieval-augmented generation (RAG) over large corpora (10GB-200GB), these optimizations enable our compute-in-SRAM system to accelerate retrieval by 4.8×-6.6× over an optimized CPU baseline, improving end-to-end RAG latency by 1.1×-1.8×. The shared off-chip memory bandwidth is modeled using a simulated HBM, while all other components are measured on the real compute-in-SRAM device. Critically, this system matches the performance of an NVIDIA A6000 GPU for RAG while being significantly more energy-efficient (54.4×-117.9× reduction). These findings validate the viability of compute-in-SRAM for complex, real-world applications and provide guidance for advancing the technology.

• This work was done during an internship at Cornell University.