2 citations · 3 across the 5 of their papers we have counts for
6 papers
Towards Training Private LLMs: Exploring Fine-Tuning Language Models on Apple Silicon with RDMA over Thunderbolt
En-Ming Huang, Yao-Ting Hsieh, Hsiang-Yu Tsou +3
Private large language model (LLM) fine-tuning is increasingly important for organizations that need to adapt models using sensitive data, but it often exceeds the memory capacity…
MDM: Manhattan Distance Mapping of DNN Weights for Parasitic-Resistance-Resilient Memristive Crossbars
Matheus Farias, Wanghley Martins, H. T. Kung
Manhattan Distance Mapping (MDM) is a post-training deep neural network (DNN) weight mapping technique for memristive bit-sliced compute-in-memory (CIM) crossbars that reduces para…
Efficient Reprogramming of Memristive Crossbars for DNNs: Weight Sorting and Bit Stucking
Matheus Farias, H. T. Kung
We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our ide…
Sorted Weight Sectioning for Energy-Efficient Unstructured Sparse DNNs on Compute-in-Memory Crossbars
Matheus Farias, H. T. Kung
We introduce (SWS): a weight allocation algorithm that places sorted deep neural network (DNN) weight sections on bit-sliced compute-in-memory (…
Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernels
Vikas Natesh, Andrew Sabot, H. T. Kung +1
We propose Rosko -- row skipping outer products -- for deriving sparse matrix multiplication (SpMM) kernels in reducing computation and memory access requirements of deep neural ne…
StitchNet: Composing Neural Networks from Pre-Trained Fragments
Surat Teerapittayanon, Marcus Comiter, Brad McDanel +1
We propose StitchNet, a novel neural network creation paradigm that stitches together fragments (one or more consecutive network layers) from multiple pre-trained neural networks.…