activity
20232026
most citedASC: Adaptive Scale Feature Map Compression for Deep Neural Network

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.NI2026

Mobile Backscatter Communication for the Battery-less Internet of Things

Weining Song, Thiemo Voigt, Stefanos Kaxiras +2

We enable backscatter communication in the battery-less mobile Internet of Things (IoT). Backscatter communication is extensively studied in static settings. Existing designs are,…

cs.AR2026

DICE: Detailed Inter-Chiplet End-to-End PHY Modeling for Accurate Chiplet Simulation

Rashid Aligholipour, Stefanos kaxiras, Yuan Yao

Scaling monolithic multicores is increasingly constrained by power/thermal limits, yield, and rising manufacturing and testing costs. Chiplet designs address these challenges by pa…

cs.AR2026

Who Needs DRAM? We Have Fiber

Hannah Atmer, Yuan Yao, Thiemo Voigt +1

The rising pressure on DRAM availability and contract pricing reflects generative AI's massive high-performance memory requirements. This pressure is heavily compounded by hypersca…

cs.AR2025

Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling

Hannah Atmer, Yuan Yao, Thiemo Voigt +1

Energy consumption dictates the cost and environmental impact of deploying Large Language Models. This paper investigates the impact of on-chip SRAM size and operating frequency on…

eess.SY2025

Efficient CNN Inference on Ultra-Low-Power MCUs via Saturation-Aware Convolution

Shiming Li, Luca Mottola, Yuan Yao +1

Quantized CNN inference on ultra-low-power MCUs incurs unnecessary computations in neurons that produce saturated output values. These values are too extreme and are eventually cla…

cs.CV2023★ 1 cited

ASC: Adaptive Scale Feature Map Compression for Deep Neural Network

Yuan Yao, Tian-Sheuan Chang

Deep-learning accelerators are increasingly in demand; however, their performance is constrained by the size of the feature map, leading to high bandwidth requirements and large bu…