Multiplier Design

Multiplier design research focuses on optimizing the efficiency and performance of multiplication operations, crucial for various applications from deep learning to general-purpose computing. Current efforts concentrate on developing novel multiplier architectures using techniques like reinforcement learning to explore the vast design space and achieve Pareto-optimal solutions balancing speed and area, as well as employing quantization and other methods to reduce computational cost and power consumption in specific applications such as edge computing. These advancements are significant because improved multiplier designs directly translate to faster, smaller, and more energy-efficient hardware for a wide range of computational tasks.

Papers

July 15, 2024

Quality Scalable Quantization Methodology for Deep Learning on Edge
Salman Abdul Khaliq, Rehan Hafiz
Deep Learning Extreme Edge Deep Learning Architecture Edge Computing Device Multiplier Design Power of Two Quantization

June 7, 2024

On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
Motahareh Sohrabi, Juan Ramirez, Tianyue H. Zhang, Simon Lacoste-Julien, Jose Gallego-Posada
Model Based Constrained Optimization Lagrange Multiplier Multiplier Design

May 10, 2024

Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs
Yao Lai, Jinxin Liu, David Z. Pan, Ping Luo
Factorial Design Adder Neural Network Multiplier Design

March 31, 2024

RL-MUL 2.0: Multiplier Design Optimization with Parallel Deep Reinforcement Learning and Space Reduction
Dongsheng Zuo, Jiadong Zhu, Yikang Ouyang, Yuzhe Ma
Reinforcement Learning Deep Reinforcement Learning Class Wise Multiplier Multiply Accumulate Multiplier Design Rl Msa

March 20, 2024

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
Xudong Sun, Nutan Chen, Alexej Gossmann, Yu Xing, Carla Feistner, Emilio Dorigatt, Felix Drost, Daniele Scarcella, Lisa Beer, Carsten Marr
Multi Objective Optimization Multi Objective Loss Landscape Multi Objective Deep Reinforcement Learning Multiplier Design

August 10, 2023

Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
Ali Cem, Ognjen Jovanovic, Siqi Yan, Yunhong Ding, Darko Zibar, Francesco Da Ros
Transfer Learning Regularization Technique Data Scarcity Photonic Chip Optical Matrix Multiplier Design

Multiplier Design

Papers

Quality Scalable Quantization Methodology for Deep Learning on Edge

On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization

Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs

RL-MUL 2.0: Multiplier Design Optimization with Parallel Deep Reinforcement Learning and Space Reduction

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling

Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning