2 citations · 8 across the 30 of their papers we have counts for
6 papers · 1 filter
Photonic Networks-on-Chip Employing Multilevel Signaling: A Cross-Layer Comparative Study
Venkata Sai Praneeth Karempudi, Febin Sunny, Ishan G Thakkar +3
Photonic network-on-chip (PNoC) architectures employ photonic links with dense wavelength-division multiplexing (DWDM) to enable high throughput on-chip transfers. Unfortunately, i…
Characterization and Mitigation of Electromigration Effects in TSV-Based Power Delivery Network Enabled 3D-Stacked DRAMs
Bobby Bose, Ishan Thakkar
With 3D-stacked DRAM architectures becoming more prevalent, it has become important to find ways to characterize and mitigate the adverse effects that can hinder their inherent acc…
ATRIA: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-DRAM CNN Processing
Supreeth Mysore Shivanandamurthy, Ishan. G. Thakkar, Sayed Ahmad Salehi
With the rapidly growing use of Convolutional Neural Networks (CNNs) in real-world applications related to machine learning and Artificial Intelligence (AI), several hardware accel…
ARXON: A Framework for Approximate Communication over Photonic Networks-on-Chip
Febin Sunny, Asif Mirza, Ishan Thakkar +2
The approximate computing paradigm advocates for relaxing accuracy goals in applications to improve energy-efficiency and performance. Recently, this paradigm has been explored to…
ODIN: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-Situ Neural Network Processing in Phase Change RAM
Supreeth Mysore Shivanandamurthy, Ishan. G. Thakkar, Sayed Ahmad Salehi
Due to the very rapidly growing use of Artificial Neural Networks (ANNs) in real-world applications related to machine learning and Artificial Intelligence (AI), several hardware a…
Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computing
Sairam Sri Vatsavai, Ishan Thakkar
To perform temporal and sequential machine learning tasks, the use of conventional Recurrent Neural Networks (RNNs) has been dwindling due to the training complexities of RNNs. To…