no code implementations • 29 Nov 2023 • Cansu Demirkiran, Guowei Yang, Darius Bunandar, Ajay Joshi
Photonic computing is a compelling avenue for performing highly efficient matrix multiplication, a crucial operation in Deep Neural Networks (DNNs).
no code implementations • 29 Nov 2023 • Farbin Fayza, Cansu Demirkiran, Hanning Chen, Che-Kai Liu, Avi Mohan, Hamza Errahmouni, Sanggeon Yun, Mohsen Imani, David Zhang, Darius Bunandar, Ajay Joshi
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN).
no code implementations • 19 Sep 2023 • Cansu Demirkiran, Lakshmi Nair, Darius Bunandar, Ajay Joshi
Our study demonstrates that analog accelerators utilizing the RNS-based approach can achieve ${\geq}99\%$ of FP32 accuracy for state-of-the-art DNN inference using data converters with only $6$-bit precision whereas a conventional analog core requires more than $8$-bit precision to achieve the same accuracy in the same DNNs.
no code implementations • 15 Jun 2023 • Cansu Demirkiran, Rashmi Agrawal, Vijay Janapa Reddi, Darius Bunandar, Ajay Joshi
In addition, we show that RNS can reduce the energy consumption of the data converters within an analog accelerator by several orders of magnitude compared to a regular fixed-point approach.
no code implementations • 1 Aug 2020 • Furkan Eris, Sadullah Canakci, Cansu Demirkiran, Ajay Joshi
To close the gap between memory and processors, and in turn improve performance, there has been an abundance of work in the area of data/instruction prefetcher designs.
