2 citations · 10 across the 21 of their papers we have counts for
22 papers
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…
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.…
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…
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.…
Sphinx: Efficiently Serving Novel View Synthesis using Regression-Guided Selective Refinement
Yuchen Xia, Souvik Kundu, Mosharaf Chowdhury +1
Novel View Synthesis (NVS) generates novel views of a scene from a sparse set of input images and is a key component in applications such as immersive rendering and 3D content gene…
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…
GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
Savini Kashmira, Jayanaka Dantanarayana, Thamirawaran Sathiyalogeswaran +3
This paper presents GraphMend, a compiler technique that automatically fixes FX graph breaks in PyTorch 2 programs. Although PyTorch 2 introduced TorchDynamo and TorchInductor to e…
TIMEST: Temporal Information Motif Estimator Using Sampling Trees
Yunjie Pan, Omkar Bhalerao, C. Seshadhri +1
The mining of pattern subgraphs, known as motifs, is a core task in the field of graph mining. Edges in real-world networks often have timestamps, so there is a need for temporal m…
DX100: A Programmable Data Access Accelerator for Indirection
Alireza Khadem, Kamalavasan Kamalakkannan, Zhenyan Zhu +8
Indirect memory accesses frequently appear in applications where memory bandwidth is a critical bottleneck. Prior indirect memory access proposals, such as indirect prefetchers, ru…
NMP-PaK: Near-Memory Processing Acceleration of Scalable De Novo Genome Assembly
Heewoo Kim, Sanjay Sri Vallabh Singapuram, Haojie Ye +4
De novo assembly enables investigations of unknown genomes, paving the way for personalized medicine and disease management. However, it faces immense computational challenges aris…
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…
MoDM: Efficient Serving for Image Generation via Mixture-of-Diffusion Models
Yuchen Xia, Divyam Sharma, Yichao Yuan +2
Diffusion-based text-to-image generation models trade latency for quality: small models are fast but generate lower-quality images, while large models produce better images but are…
Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics
Yichao Yuan, Advait Iyer, Lin Ma +1
Despite the high computational throughput of GPUs, limited memory capacity and bandwidth-limited CPU-GPU communication via PCIe links remain significant bottlenecks for acceleratin…
Multi-Dimensional Vector ISA Extension for Mobile In-Cache Computing
Alireza Khadem, Daichi Fujiki, Hilbert Chen +4
In-cache computing technology transforms existing caches into long-vector compute units and offers low-cost alternatives to building expensive vector engines for mobile CPUs. Unfor…
Palermo: Improving the Performance of Oblivious Memory using Protocol-Hardware Co-Design
Haojie Ye, Yuchen Xia, Yuhan Chen +6
Oblivious RAM (ORAM) hides the memory access patterns, enhancing data privacy by preventing attackers from discovering sensitive information based on the sequence of memory accesse…
Accurate and Fast Estimation of Temporal Motifs using Path Sampling
Yunjie Pan, Omkar Bhalerao, C. Seshadhri +1
Counting the number of small subgraphs, called motifs, is a fundamental problem in social network analysis and graph mining. Many real-world networks are directed and temporal, whe…
Understanding the Performance and Estimating the Cost of LLM Fine-Tuning
Yuchen Xia, Jiho Kim, Yuhan Chen +4
Due to the cost-prohibitive nature of training Large Language Models (LLMs), fine-tuning has emerged as an attractive alternative for specializing LLMs for specific tasks using lim…
Toward Cross-Layer Energy Optimizations in AI Systems
Jae-Won Chung, Nishil Talati, Mosharaf Chowdhury
The "AI for Science, Energy, and Security" report from DOE outlines a significant focus on developing and optimizing artificial intelligence workflows for a foundational impact on…
Demystifying Graph Sparsification Algorithms in Graph Properties Preservation
Yuhan Chen, Haojie Ye, Sanketh Vedula +4
Graph sparsification is a technique that approximates a given graph by a sparse graph with a subset of vertices and/or edges. The goal of an effective sparsification algorithm is t…
Everest: GPU-Accelerated System For Mining Temporal Motifs
Yichao Yuan, Haojie Ye, Sanketh Vedula +2
Temporal motif mining is the task of finding the occurrences of subgraph patterns within a large input temporal graph that obey the specified structural and temporal constraints. D…
Vector-Processing for Mobile Devices: Benchmark and Analysis
Alireza Khadem, Daichi Fujiki, Nishil Talati +2
Vector processing has become commonplace in today's CPU microarchitectures. Vector instructions improve performance and energy which is crucial for resource-constraint mobile devic…
Accelerating Graph Analytics on a Reconfigurable Architecture with a Data-Indirect Prefetcher
Yichen Yang, Jingtao Li, Nishil Talati +5
The irregular nature of memory accesses of graph workloads makes their performance poor on modern computing platforms. On manycore reconfigurable architectures (MRAs), in particula…