RaPiD: AI Accelerator for Ultra-Low Precision Training and Inference
- Swagath Venkataramani
- Vijayalakshmi Srinivasan
- et al.
- 2021
- ISCA 2021
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14 results for Shubham Jain
BiScaled-DNN: Quantizing long-tailed datastructures with two scale factors for deep neural networks
- Shubham Jain
- Swagath Venkataramani
- et al.
- 2019
- DAC 2019
Compensated-DNN: Energy efficient low-precision deep neural networks by compensating quantization errors
- Shubham Jain
- Swagath Venkataramani
- et al.
- 2018
- DAC 2018
SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks
- Sanchari Sen
- Shubham Jain
- et al.
- 2019
- IEEE TC
Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited)
- Shubham Jain
- Ching-Tzu Chen
- et al.
- 2023
- ISCAS 2023
Impact 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
Design Tools for Resistive Crossbar based Machine Learning Accelerators
- Indranil Chakraborty
- Sourjya Roy
- et al.
- 2021
- AICAS 2021
Emerging Neural Workloads and Their Impact on Hardware
- David Brooks
- Martin M. Frank
- et al.
- 2020
- DATE 2020
A 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
Ax-BxP: Approximate Blocked Computation for Precision-reconfigurable Deep Neural Network Acceleration
- Reena Elangovan
- Shubham Jain
- et al.
- 2022
- ACM TODAES