4 papers
Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
Software-hardware co-design is essential for optimizing in-memory computing (IMC) hardware accelerators for neural networks. However, most existing optimization frameworks target a…
Mixed-Precision Quantization for Language Models: Techniques and Prospects
Mariam Rakka, Marios Fournarakis, Olga Krestinskaya +5
The rapid scaling of language models (LMs) has resulted in unprecedented computational, memory, and energy requirements, making their training and deployment increasingly unsustain…
CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
To maximize hardware efficiency and performance accuracy in Compute-In-Memory (CIM)-based neural network accelerators for Artificial Intelligence (AI) applications, co-optimizing b…
Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
Designing generalized in-memory computing (IMC) hardware that efficiently supports a variety of workloads requires extensive design space exploration, which is infeasible to perfor…