Demonstration of transformer-based ALBERT model on a 14nm analog AI inference chip
- An Chen
- Stefano Ambrogio
- et al.
- 2025
- Nature Communications
Paper
Publications
53 results for Pritish Narayanan
Analog AI Accelerators for Transformer-based Language Models: Hardware, Workload, and Power Performance
- H. Tsai
- H. Benmeziane
- et al.
- 2025
- IMW 2025
Conference paperHardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
- Malte J. Rasch
- Charles Mackin
- et al.
- 2023
- Nature Communications
PaperAn analog-AI chip for energy-efficient speech recognition and transcription
- S. Ambrogio
- Pritish Narayanan
- et al.
- 2023
- Nature
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
PaperOptimised weight programming for analogue memory-based deep neural networks
- Charles Mackin
- Malte J. Rasch
- et al.
- 2022
- Nature Communications
PaperAnalog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers
- Atsuya Okazaki
- Pritish Narayanan
- et al.
- 2022
- ISCAS 2022
Conference paperFully On-Chip MAC at 14 nm Enabled by Accurate Row-Wise Programming of PCM-Based Weights and Parallel Vector-Transport in Duration-Format
- Pritish Narayanan
- Stefano Ambrogio
- et al.
- 2021
- IEEE T-ED
PaperNoise-Resilient DNN: Tolerating Noise in PCM-Based AI Accelerators via Noise-Aware Training
- Sanjay Kariyappa
- Hsinyu Tsai
- et al.
- 2021
- IEEE T-ED
PaperToward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices
- Katherine Spoon
- Hsinyu Tsai
- et al.
- 2021
- Frontiers in Computational Neuroscience
Paper