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cs.AR2026
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…
cs.AR2023★ 1 cited
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…