65 citations · 90 across the 7 of their papers we have counts for
4 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…
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…
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…