EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, and G
3 Pith papers cite this work. Polarity classification is still indexing.
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GPIR achieves up to 297 times higher throughput than prior GPU PIR systems by fusing operations in stages and using pipelined transposed layouts to cut DRAM traffic during batched lattice-based queries.
ZKMLOps is an MLOps framework that uses zero-knowledge proofs to generate verifiable cryptographic evidence of AI model compliance without revealing confidential information.
citing papers explorer
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EasyRider: Mitigating Power Transients in Datacenter-Scale Training Workloads
EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
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GPIR: Enabling Practical Private Information Retrieval with GPUs
GPIR achieves up to 297 times higher throughput than prior GPU PIR systems by fusing operations in stages and using pipelined transposed layouts to cut DRAM traffic during batched lattice-based queries.
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"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems
ZKMLOps is an MLOps framework that uses zero-knowledge proofs to generate verifiable cryptographic evidence of AI model compliance without revealing confidential information.