  As advanced machine learning systems' capabilities begin to play a
significant role in geopolitics and societal order, it may become imperative
that (1) governments be able to enforce rules on the development of advanced ML
systems within their borders, and (2) countries be able to verify each other's
compliance with potential future international agreements on advanced ML
development. This work analyzes one mechanism to achieve this, by monitoring
the computing hardware used for large-scale NN training. The framework's
primary goal is to provide governments high confidence that no actor uses large
quantities of specialized ML chips to execute a training run in violation of
agreed rules. At the same time, the system does not curtail the use of consumer
computing devices, and maintains the privacy and confidentiality of ML
practitioners' models, data, and hyperparameters. The system consists of
interventions at three stages: (1) using on-chip firmware to occasionally save
snapshots of the the neural network weights stored in device memory, in a form
that an inspector could later retrieve; (2) saving sufficient information about
each training run to prove to inspectors the details of the training run that
had resulted in the snapshotted weights; and (3) monitoring the chip supply
chain to ensure that no actor can avoid discovery by amassing a large quantity
of un-tracked chips. The proposed design decomposes the ML training rule
verification problem into a series of narrow technical challenges, including a
new variant of the Proof-of-Learning problem [Jia et al. '21].
