1 citations · 1 across the 4 of their papers we have counts for
4 papers
Design Rules for Extreme-Edge Scientific Computing on AI Engines
Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos +3
Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and throughput requirements demand sma…
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
Tommaso Baldi, Javier Campos, Olivia Weng +4
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensin…
Reliable edge machine learning hardware for scientific applications
Tommaso Baldi, Javier Campos, Ben Hawks +15
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementatio…
Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications
Olivia Weng, Alexander Redding, Nhan Tran +2
With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters s…