65 citations · 140 across the 14 of their papers we have counts for
7 papers · 1 filter
Towards Efficient Hyperdimensional Computing Using Photonics
Farbin Fayza, Cansu Demirkiran, Hanning Chen +8
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-lin…
Mirage: An RNS-Based Photonic Accelerator for DNN Training
Cansu Demirkiran, Guowei Yang, Darius Bunandar +1
Photonic computing is a compelling avenue for performing highly efficient matrix multiplication, a crucial operation in Deep Neural Networks (DNNs). While this method has shown gre…
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…
Leveraging Residue Number System for Designing High-Precision Analog Deep Neural Network Accelerators
Cansu Demirkiran, Rashmi Agrawal, Vijay Janapa Reddi +2
Achieving high accuracy, while maintaining good energy efficiency, in analog DNN accelerators is challenging as high-precision data converters are expensive. In this paper, we over…
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…