1 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
DreamRAM: A Fine-Grained Configurable Design Space Modeling Tool for Custom 3D Die-Stacked DRAM
Victor Cai, Jennifer Zhou, Haebin Do +2
3D die-stacked DRAM has emerged as a key technology for delivering high bandwidth and high density for applications such as high-performance computing, graphics, and machine learni…
PRISM: Probabilistic Runtime Insights and Scalable Performance Modeling for Large-Scale Distributed Training
Alicia Golden, Michael Kuchnik, Samuel Hsia +4
Large model training beyond tens of thousands of GPUs is an uncharted territory. At such scales, disruptions to the training process are not a matter of if, but a matter of when --…
Modeling PFAS in Semiconductor Manufacturing to Quantify Trade-offs in Energy Efficiency and Environmental Impact of Computing Systems
Mariam Elgamal, Abdulrahman Mahmoud, Gu-Yeon Wei +2
The electronics and semiconductor industry is a prominent consumer of per- and poly-fluoroalkyl substances (PFAS), also known as forever chemicals. PFAS are persistent in the envir…
The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute
Yunho Jin, Gu-Yeon Wei, David Brooks
Scaling large language models (LLMs) has driven significant advancements, yet it faces diminishing returns and escalating energy demands. This work explores how test-time compute (…
FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices
Yuji Chai, Mujin Kwen, Mujin Kwun +2
Deploying LLMs on edge devices presents serious technical challenges. Memory elasticity is crucial for edge devices with unified memory, where memory is shared and fluctuates dynam…