Researchers from KAUST and Compumacy for Artificial Intelligence Solutions have released “Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators”.
Abstract
“Software-hardware co-design is essential for optimizing in-memory computing (IMC) hardware accelerators for neural networks. However, most existing optimization frameworks target a single workload, leading to highly specialized hardware designs that do not generalize well across models and applications. In contrast, practical deployment scenarios require a single IMC platform that can efficiently support multiple neural network workloads. This work presents a joint hardware-workload co-optimization framework based on an optimized evolutionary algorithm for designing generalized IMC accelerator architectures. By explicitly capturing cross-workload trade-offs rather than optimizing for a single model, the proposed approach significantly reduces the performance gap between workload-specific and generalized IMC designs. The framework is evaluated on both RRAM- and SRAM-based IMC architectures, demonstrating strong robustness and adaptability across diverse design scenarios. Compared to baseline methods, the optimized designs achieve energy-delay-area product (EDAP) reductions of up to 76.2% and 95.5% when optimizing across a small set (4 workloads) and a large set (9 workloads), respectively. The source code of the framework is available at this https URL.”
Find the technical paper here. March 2026.
Citation: Krestinskaya, Olga, Mohammed E. Fouda, Ahmed Eltawil, and Khaled N. Salama. “Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators.” arXiv:2603.03880, March 2026. https://doi.org/10.48550/arXiv.2603.03880.
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