Privatar uses horizontal frequency partitioning and distribution-aware minimal perturbation to enable private offloading of VR avatar reconstruction, supporting 2.37x more users with modest overhead.
Lee, David Brooks, and Carole-Jean Wu
6 Pith papers cite this work. Polarity classification is still indexing.
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FlexiFlow optimizes carbon footprint for item-level intelligence on flexible electronics by modeling lifetime variation, delivering 1.62X microarchitectural and 14.5X algorithmic reductions plus a 30.9 kHz tape-out.
GreenCache dynamically manages LLM KV cache resources to reduce carbon emissions by 15.1% on average (up to 25.3%) while meeting latency constraints for over 90% of requests on real traces.
Quantitative benchmarks across recent AI accelerators reveal that optimal hardware choice varies with workload parameters and that several platforms incur substantially higher idle power than GPUs.
A compact QUBO encoding derived via ILP reduces logical variables by thousands in AES, MD5, SHA1 and SHA256, with over 8x reduction for AES-256.
M100 is a tensor-based dataflow architecture that eliminates heavy caching through compiler-managed data streams, claiming higher utilization and better performance than GPGPUs for AD and LLM inference tasks.
citing papers explorer
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Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
Privatar uses horizontal frequency partitioning and distribution-aware minimal perturbation to enable private offloading of VR avatar reconstruction, supporting 2.37x more users with modest overhead.
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Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge
FlexiFlow optimizes carbon footprint for item-level intelligence on flexible electronics by modeling lifetime variation, delivering 1.62X microarchitectural and 14.5X algorithmic reductions plus a 30.9 kHz tape-out.
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Cache Your Prompt When It's Green: Carbon-Aware Caching for Large Language Model Serving
GreenCache dynamically manages LLM KV cache resources to reduce carbon emissions by 15.1% on average (up to 25.3%) while meeting latency constraints for over 90% of requests on real traces.
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The xPU-athalon: Quantifying the Competition of AI Acceleration
Quantitative benchmarks across recent AI accelerators reveal that optimal hardware choice varies with workload parameters and that several platforms incur substantially higher idle power than GPUs.
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A compact QUBO encoding of computational logic formulae demonstrated on cryptography constructions
A compact QUBO encoding derived via ILP reduces logical variables by thousands in AES, MD5, SHA1 and SHA256, with over 8x reduction for AES-256.
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M100: An Orchestrated Dataflow Architecture Powering General AI Computing
M100 is a tensor-based dataflow architecture that eliminates heavy caching through compiler-managed data streams, claiming higher utilization and better performance than GPGPUs for AD and LLM inference tasks.