3 citations · 8 across the 8 of their papers we have counts for
12 papers
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization
Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantiz…
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks
Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty +2
Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applicatio…
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…
SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning
Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Sparse neural networks can greatly facilitate the deployment of neural networks on resource-constrained platforms as they offer compact model sizes while retaining inference accura…
ROBIN: A Robust Optical Binary Neural Network Accelerator
Febin P. Sunny, Asif Mirza, Mahdi Nikdast +1
Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerato…
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…