20 citations · 23 across the 4 of their papers we have counts for
5 papers
Panacea: Novel DNN Accelerator using Accuracy-Preserving Asymmetric Quantization and Energy-Saving Bit-Slice Sparsity
Dongyun Kam, Myeongji Yun, Sunwoo Yoo +3
Low bit-precisions and their bit-slice sparsity have recently been studied to accelerate general matrix-multiplications (GEMM) during large-scale deep neural network (DNN) inferenc…
Block Orthogonal Sparse Superposition Codes for Ultra-Reliable Low-Latency Communications
Donghwa Han, Jeonghun Park, Youngjoo Lee +2
Low-rate and short-packet transmissions are important for ultra-reliable low-latency communications (URLLC). In this paper, we put forth a new family of sparse superposition codes…
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
Gunho Park, Baeseong Park, Minsub Kim +7
Recent advances in self-supervised learning and the Transformer architecture have significantly improved natural language processing (NLP), achieving remarkably low perplexity. How…
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks
Ranggi Hwang, Minhoo Kang, Jiwon Lee +3
Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its…
Design and Evaluation Frameworks for Advanced RISC-based Ternary Processor
Dongyun Kam, Jung Gyu Min, Jongho Yoon +3
In this paper, we introduce the design and verification frameworks for developing a fully-functional emerging ternary processor. Based on the existing compiling environments for bi…