Algorithm-Hardware Co-Design

AI System design with hardware-friendly algorithm

Summary:

- Low-bit AI Framework for on-device personalization & quantized MAC operating system design

- A low-cost convergence monitoring system for computation skip in DNN training

Reference:

- Seungkyu Choi, Jaekang Shin, Yeongjae Choi, and Lee-Sup Kim, "An Optimized Design Technique of Low-bit Neural Network Training for Personalization on IoT Devices," ACM/IEEE Design Automation Conference (DAC), 2019.

- Seungkyu Choi, Jaekang Shin, and Lee-Sup Kim, "A Convergence Monitoring Method for DNN Training of On-device Task Adaptation," IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2021.

Algorithm-Hardware co-design for efficient dnn processing

Summary:

The proposed algorithmic scheme for multi-task DNN reduces per-task weight and activation size by sharing those data between tasks. We design architecture and dataflow to minimize DRAM access by fully utilizing the benefits.

Reference:

Jaekang Shin, Seungkyu Choi, Jongwoo Ra, and Lee-Sup Kim, "Algorithm/Architecture Co-Design for Energy-Efficient Acceleration of Multi-Task DNN," ACM/IEEE Design Automation Conference (DAC), 2022.