11 citations · 19 across the 5 of their papers we have counts for
5 papers
Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection
Lakshmi Nair, David Widemann, Brad Turcott +6
Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware. Advances in photonic computing can have profound…
A Blueprint for Precise and Fault-Tolerant Analog Neural Networks
Cansu Demirkiran, Lakshmi Nair, Darius Bunandar +1
Analog computing has reemerged as a promising avenue for accelerating deep neural networks (DNNs) due to its potential to overcome the energy efficiency and scalability challenges…
INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers
Lakshmi Nair, Mikhail Bernadskiy, Arulselvan Madhavan +3
The recent rise of large language models (LLMs) has resulted in increased efforts towards running LLMs at reduced precision. Running LLMs at lower precision supports resource const…
Sensitivity-Aware Finetuning for Accuracy Recovery on Deep Learning Hardware
Lakshmi Nair, Darius Bunandar
Existing methods to recover model accuracy on analog-digital hardware in the presence of quantization and analog noise include noise-injection training. However, it can be slow in…
Adaptive Block Floating-Point for Analog Deep Learning Hardware
Ayon Basumallik, Darius Bunandar, Nicholas Dronen +6
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts. However, recent studies show that DNNs…