1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
Towards Memory Specialization: A Case for Long-Term and Short-Term RAM
Peijing Li, Muhammad Shahir Abdurraman, Rachel Cleaveland +6
Both SRAM and DRAM have stopped scaling: there is no technical roadmap to reduce their cost (per byte/GB). As a result, memory now dominates system cost. This paper argues for a pa…
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…
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…