32 citations · 94 across the 11 of their papers we have counts for
11 papers · 1 filter
The Unlikely Hero: Nonideality in Analog Photonic Neural Networks as Built-in Defender Against Adversarial Attacks
Haotian Lu, Ziang Yin, Partho Bhoumik +3
Electronic-photonic computing systems have emerged as a promising platform for accelerating deep neural network (DNN) workloads. Major efforts have been focused on countering hardw…
DOCTOR: Dynamic On-Chip Temporal Variation Remediation Toward Self-Corrected Photonic Tensor Accelerators
Haotian Lu, Sanmitra Banerjee, Jiaqi Gu
Photonic computing has emerged as a promising solution for accelerating computation-intensive artificial intelligence (AI) workloads, offering unparalleled speed and energy efficie…
Compact and Low-Loss PCM-based Silicon Photonic MZIs for Photonic Neural Networks
Amin Shafiee, Sanmitra Banerjee, Benoit Charbonnier +2
We present an optimized Mach-Zehnder Interferometer (MZI) with phase change materials for photonic neural networks (PNNs). With 0.2 dB loss, -38 dB crosstalk, and length of 52 micr…
Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks
Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty +2
With the continuous increase in the size and complexity of machine learning models, the need for specialized hardware to efficiently run such models is rapidly growing. To address…
Characterizing Coherent Integrated Photonic Neural Networks under Imperfections
Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty
Integrated photonic neural networks (IPNNs) are emerging as promising successors to conventional electronic AI accelerators as they offer substantial improvements in computing spee…
Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations
Asif Mirza, Amin Shafiee, Sanmitra Banerjee +3
Silicon-photonic neural networks (SPNNs) have emerged as promising successors to electronic artificial intelligence (AI) accelerators by offering orders of magnitude lower latency…
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…
Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis
Sanmitra Banerjee, Mahdi Nikdast, Sudeep Pasricha +1
Singular-value-decomposition-based coherent integrated photonic neural networks (SC-IPNNs) have a large footprint, suffer from high static power consumption for training and infere…
CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networks
Sanmitra Banerjee, Mahdi Nikdast, Sudeep Pasricha +1
We propose a novel hardware-aware magnitude pruning technique for coherent photonic neural networks. The proposed technique can prune 99.45% of network parameters and reduce the st…
Optimizing Coherent Integrated Photonic Neural Networks under Random Uncertainties
Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty
We propose an optimization method to improve power efficiency and robustness in silicon-photonic-based coherent integrated photonic neural networks. Our method reduces the network…
Modeling Silicon-Photonic Neural Networks under Uncertainties
Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty
Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the ene…