2 citations · 10 across the 21 of their papers we have counts for
10 papers · 1 filter
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…