EdgeCIM: A Hardware-Software Co-Design for CIM-Based Acceleration of Small Language Models
The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, and embedded platforms, has exposed fundamental inefficiencies in existing accelerators. While GPUs handle prefill workloads efficiently, the autoregressive decoding phase is dominated by GEMV operations that are inherently memory-bound, resulting in poor utilization and prohibitive energy costs at the edge. In this work, we present EdgeCIM, a hardware-software co-design framework that rethinks accelerator design for end-to-end decoder-only inference. At its core is a CIM macro, implemented in 65nm, coupled with a tile-based mapping strategy that balances pipeline stages, maximiz...
| Authors | Jinane Bazzi, Mariam Rakka, Fadi Kurdahi et al. |
|---|---|
| Primary category | cs.AR |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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