4 papers
Heterogeneous Memory Design Exploration for AI Accelerators with a Gain Cell Memory Compiler
Xinxin Wang, Lixian Yan, Shuhan Liu +10
As memory increasingly dominates system cost and energy, heterogeneous on-chip memory systems that combine technologies with complementary characteristics are becoming essential. G…
The Future of Memory: Limits and Opportunities
Samuel Dayo, Shuhan Liu, Peijing Li +5
Memory latency, bandwidth, capacity, and energy increasingly limit performance. In this paper, we reconsider proposed system architectures that consist of huge (many-terabyte to pe…
GainSight: A Unified Framework for Data Lifetime Profiling and Heterogeneous Memory Composition
Peijing Li, Matthew Hung, Yiming Tan +8
As AI workloads drive increasing memory requirements, domain-specific accelerators need higher-density on-chip memory beyond what current SRAM scaling trends can provide. Simultane…
OpenGCRAM: An Open-Source Gain Cell Compiler Enabling Design-Space Exploration for AI Workloads
Xinxin Wang, Lixian Yan, Shuhan Liu +10
Gain Cell memory (GCRAM) offers higher density and lower power than SRAM, making it a promising candidate for on-chip memory in domain-specific accelerators. To support workloads w…