Tech Xplore

AI models stumble on basic multiplication without special training methods, study finds

These days, large language models can handle increasingly complex tasks, writing complex code and engaging in sophisticated ...
IEEE

Multiplication-Free Lookup-Based CNN Accelerator Using Residual Vector Quantization and Its FPGA Implementation

Abstract: In this paper, a table lookup-based computing technique is proposed to perform convolutional neural network (CNN) inference without multiplication, and its FPGA implementation is ...
GitHub

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...
IEEE

HSAMM: A Hybrid-Strassen Algorithm-Based Asynchronous Architecture for Sparse Matrix Multiplication

Abstract: General sparse matrix-matrix multiplication (SpGEMM) is a fundamental computational method with wide-ranging applications in scientific simulations, machine learning, and image processing.