Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators
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 si...
| Authors | Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil et al. |
|---|---|
| Primary category | cs.AR |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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