1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
The xPU-athalon: Quantifying the Competition of AI Acceleration
Alicia Golden, Carole-Jean Wu, Gu-Yeon Wei +1
The push for greater efficiency in AI computation has given rise to an array of accelerator architectures that increasingly challenge the GPU's long-standing dominance. In this wor…
RPU -- A Reasoning Processing Unit
Matthew Adiletta, Gu-Yeon Wei, David Brooks
Large language model (LLM) inference performance is increasingly bottlenecked by the memory wall. While GPUs continue to scale raw compute throughput, they struggle to deliver scal…
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…
CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device Learning
Sai Qian Zhang, Thierry Tambe, Nestor Cuevas +2
On-device learning allows AI models to adapt to user data, thereby enhancing service quality on edge platforms. However, training AI on resource-limited devices poses significant c…