3 papers
cs.LG2025
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 (…
cs.AI2025
FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices
Yuji Chai, Mujin Kwen, David Brooks +1
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…
cs.AR2024
Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models
Yun-Chen Lo, Gu-Yeon Wei, David Brooks
As cutting-edge large language models (LLMs) continue to transform various industries, their fast-growing model size and sequence length have led to memory traffic and capacity cha…