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20232026
most citedNMP-PaK: Near-Memory Processing Acceleration of Scalable De Novo Genome Assembly

2 citations · 6 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.DC2026

Energy Calculus: A Compositional Algebra of Energy in Computational Systems

Mosharaf Chowdhury, Jae-Won Chung, Jeff J. Ma +2

Energy is a binding constraint for AI scaling, yet it lacks the formal treatment that computation, communication, and learning have long enjoyed. Recent systems demonstrate large e…

cs.DC2026

Agentic AI Workload Characteristics

Yichao Yuan, Ankita Nayak, Souvik Kundu +1

Agentic AI shifts LLM serving from isolated prompt-generation requests to stateful, multi-turn executions that repeatedly invoke the model, call tools, and grow context over time.…

cs.DC2026

KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving

Yichao Yuan, Mosharaf Chowdhury, Nishil Talati

Power has become a central bottleneck for AI inference. This problem is becoming more urgent as agentic AI emerges as a major workload class, yet prior power-management techniques…

cs.DC2026

BlazingAML: High-Throughput Anti-Money Laundering (AML) via Multi-Stage Graph Mining

Haojie Ye, Arjun Laxman, Yichao Yuan +2

Money laundering detection faces challenges due to excessive false positives and inadequate adaptation to sophisticated multi-stage schemes that exploit modern financial networks.…

cs.DC2025

ZKProphet: Understanding Performance of Zero-Knowledge Proofs on GPUs

Tarunesh Verma, Yichao Yuan, Nishil Talati +1

Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about t…

cs.DC2025

MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints

Yichao Yuan, Lin Ma, Nishil Talati

Mixture of Experts (MoE) LLMs, characterized by their sparse activation patterns, offer a promising approach to scaling language models while avoiding proportionally increasing the…