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.

在极端实验环境下，通过开发和验证可靠的算法，解决了 ML 边缘处理中的验证挑战，包括在实验软件框架中实现比特精确的性能验证，验证在极端量化和修剪下的 ML 模型的稳健性，以及实现高精度的模型检查以实现高效容错。该研究探讨了开发强健算法的方法，提出了初步结果和缓解策略，并展望未来研究的方向，致力于实现加速科学发现的自主科学实验方法的长远目标。