Reliable edge machine learning hardware for scientific applications
IEEE VLSI Test Symposium(2024)
摘要
Extreme data rate scientific experiments create massive amounts of data that
require efficient ML edge processing. This leads to unique validation
challenges for VLSI implementations of ML algorithms: enabling bit-accurate
functional simulations for performance validation in experimental software
frameworks, verifying those ML models are robust under extreme quantization and
pruning, and enabling ultra-fine-grained model inspection for efficient fault
tolerance. We discuss approaches to developing and validating reliable
algorithms at the scientific edge under such strict latency, resource, power,
and area requirements in extreme experimental environments. We study metrics
for developing robust algorithms, present preliminary results and mitigation
strategies, and conclude with an outlook of these and future directions of
research towards the longer-term goal of developing autonomous scientific
experimentation methods for accelerated scientific discovery.
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