2 citations · 2 across the 13 of their papers we have counts for
14 papers
Hopfield Networks in Non-volatile Memory
Patrick Miller, Ulya R. Karpuzcu
Many important computational workloads are bound by the performance of the memory system that supplies data to conventional processors. Compute in Memory (CiM) promises to circumve…
MetaCluster: Enabling Deep Compression of Kolmogorov-Arnold Network
Matthew Raffel, Adwaith Renjith, Lizhong Chen
Kolmogorov-Arnold Networks (KANs) replace scalar weights with per-edge vectors of basis coefficients, thereby increasing expressivity and accuracy while also resulting in a multipl…
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation System
Matthew Raffel, Victor Agostinelli, Lizhong Chen
This paper discusses the construction, fine-tuning, and deployment of BeaverTalk, a cascaded system for speech-to-text translation as part of the IWSLT 2025 simultaneous translatio…
FlashKAT: Understanding and Addressing Performance Bottlenecks in the Kolmogorov-Arnold Transformer
Matthew Raffel, Lizhong Chen
The Kolmogorov-Arnold Network (KAN) has been gaining popularity as an alternative to the multilayer perceptron (MLP) due to its greater expressiveness and interpretability. Even so…
Towards Universal Semantics With Large Language Models
Raymond Baartmans, Matthew Raffel, Rahul Vikram +2
The Natural Semantic Metalanguage (NSM) is a linguistic theory based on a universal set of semantic primes: simple, primitive word-meanings that have been shown to exist in most, i…
ML For Hardware Design Interpretability: Challenges and Opportunities
Raymond Baartmans, Andrew Ensinger, Victor Agostinelli +1
The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. How…
Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions
Tahmid Hasan Prato, Seijoon Kim, Lizhong Chen +1
Deep neural networks are not resilient to parameter corruptions: even a single-bitwise error in their parameters in memory can cause an accuracy drop of over 10%, and in the worst…
MatrixKAN: Parallelized Kolmogorov-Arnold Network
Cale Coffman, Lizhong Chen
Kolmogorov-Arnold Networks (KAN) are a new class of neural network architecture representing a promising alternative to the Multilayer Perceptron (MLP), demonstrating improved expr…
Swift: High-Performance Sparse Tensor Contraction for Scientific Applications
Andrew Ensinger, Gabriel Kulp, Victor Agostinelli +2
In scientific fields such as quantum computing, physics, chemistry, and machine learning, high dimensional data are typically represented using sparse tensors. Tensor contraction i…
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking
Yifan Zeng, Ojas Tendolkar, Raymond Baartmans +3
Ranking passages by prompting a large language model (LLM) can achieve promising performance in modern information retrieval (IR) systems. A common approach to sort the ranking lis…
LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions
Victor Agostinelli, Sanghyun Hong, Lizhong Chen
A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions…
Simultaneous Masking, Not Prompting Optimization: A Paradigm Shift in Fine-tuning LLMs for Simultaneous Translation
Matthew Raffel, Victor Agostinelli, Lizhong Chen
Large language models (LLMs) have achieved state-of-the-art performance in various language processing tasks, motivating their adoption in simultaneous translation. Current fine-tu…
FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction
Gabriel Kulp, Andrew Ensinger, Lizhong Chen
Tensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking a…
Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models
Victor Agostinelli, Max Wild, Matthew Raffel +2
Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety…