Analog AI Accelerators for Transformer-based Language Models: Hardware, Workload, and Power Performance
- H. Tsai
- H. Benmeziane
- et al.
- 2025
- IMW 2025
Conference paper
Publications
19 results for Shubham Jain
Analog-AI Hardware Accelerators for Low-Latency Transformer-Based Language Models (Invited)
- G.W. Burr
- H. Tsai
- et al.
- 2025
- CICC 2025
Conference paperA Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC
- Monodeep Kar
- Joel Silberman
- et al.
- 2024
- ISSCC 2024
Conference paperDNNDaSher: A Compiler Framework for Dataflow Compatible End-to-End Acceleration on IBM AIU
- Sanchari Sen
- Shubham Jain
- et al.
- 2024
- IEEE Micro
PaperPower-Limited Inference Performance Optimization Using a Software-Assisted Peak Current Regulation Scheme in a 5-nm AI SoC
- Monodeep Kar
- Joel Silberman
- et al.
- 2024
- IEEE Journal of Solid-State Circuits
PaperArchitectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited)
- Shubham Jain
- Ching-Tzu Chen
- et al.
- 2023
- ISCAS 2023
Conference paperImpact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited)
- Martin M. Frank
- Ning Li
- et al.
- 2023
- IRPS 2023
Conference paperA Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh
- Shubham Jain
- Hsinyu Tsai
- et al.
- 2023
- IEEE Transactions on VLSI Systems
PaperAx-BxP: Approximate Blocked Computation for Precision-reconfigurable Deep Neural Network Acceleration
- Reena Elangovan
- Shubham Jain
- et al.
- 2022
- ACM TODAES
PaperRaPiD: AI Accelerator for Ultra-Low Precision Training and Inference
- Swagath Venkataramani
- Vijayalakshmi Srinivasan
- et al.
- 2021
- ISCA 2021
Conference paper