8 papers · 1 filter
BARTER: Compact Dynamic Sparse Training with Standard Dense Kernels
Yueyuan Sui, Payal Mohapatra, Qi Zhu +1
Dynamic sparse training (DST) aims to reduce training costs while maintaining accuracy. Existing DST methods mask out parameters but keep matrix dimensions unchanged. Training spee…
Source-Learned Reliance for Selective Test-Time Adaptation of Multimodal Time Series
Payal Mohapatra, Yueyuan Sui, Haodong Yang +3
Multimodal wearable systems must remain reliable when sensor streams become noisy or unavailable. Existing multimodal test-time adaptation (TTA) methods often assess reliability on…
Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion
Payal Mohapatra, Haodong Yang, Yueyuan Sui +3
Multimodal systems often encode every available input, even when a subset suffices for prediction. Adaptive acquisition can reduce this cost by using predictions from incrementally…
PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling
Weisi Yang, Stephen Xia
Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased privacy, personalization, and red…
Depth Exploration for LLM Decoding
Weisi Yang, Zipeng Sun, Stephen Xia
Autoregressive LLM decoding evaluates every generated token through the full layer stack, even though many tokens become predictable at intermediate depths. Existing lossless depth…
Attention Drift: What Autoregressive Speculative Decoding Models Learn
Doğaç Eldenk, Payal Mohapatra, Yigitcan Comlek +3
Speculative decoding accelerates LLM inference by drafting future tokens with a small model, but drafter models degrade sharply under template perturbation and long-context inputs.…
Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference
Yueyuan Sui, Payal Mohapatra, Doğaç Eldenk +5
Edge devices increasingly run multimodal sensing pipelines that must remain accurate despite fluctuating power budgets and unpredictable sensor dropout. Existing pruning methods fa…
UNIFERENCE: A Discrete Event Simulation Framework for Developing Distributed AI Models
Doğaç Eldenk, Stephen Xia
Developing and evaluating distributed inference algorithms remains difficult due to the lack of standardized tools for modeling heterogeneous devices and networks. Existing studies…