5 papers
Systolic Array-based Accelerator for Structured State-Space Models
Shiva Raja, Cansu Demirkiran, Aakash Sarkar +2
Sequence modeling is crucial for AI to understand temporal data and detect complex time-dependent patterns. While recurrent neural networks (RNNs), convolutional neural networks (C…
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…
Custom Tailored Suite of Random Forests for Prefetcher Adaptation
Furkan Eris, Sadullah Canakci, Cansu Demirkiran +1
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. Prefetchers…