SWhat changed
Developed domain-specific machine-learning systems: TPU v1 integer systolic inference;TPU v2/v3 training co-design with fast inter-chip communication;TPU v4 optically reconfigurable topology and embedding-oriented SparseCores.
Scientific scope
Developed domain-specific machine-learning systems: TPU v1 integer systolic inference;TPU v2/v3 training co-design with fast inter-chip communication;TPU v4 optically reconfigurable topology and embedding-oriented SparseCores. One evolving hardware family with separate2017,2020and2023 paper author sets;2015/2017/2020deployment dates are not publication dates. Workload-specific comparisons and peak TOPS are not general current-device superiority. TPU v4 carbon comparisons include datacenter/power-supply assumptions, not chip-only effects;SparseCores existed in earlier generations. Paper's TensorCore is not NVIDIA's Tensor Core.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. One evolving hardware family with separate2017,2020and2023 paper author sets;2015/2017/2020deployment dates are not publication dates. Workload-specific comparisons and peak TOPS are not general current-device superiority. TPU v4 carbon comparisons include datacenter/power-supply assumptions, not chip-only effects;SparseCores existed in earlier generations. Paper's TensorCore is not NVIDIA's Tensor Core.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- In-Datacenter Performance Analysis of a Tensor Processing Unit
- A Domain-Specific Supercomputer for Training Deep Neural Networks
- TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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