317 research outputs found
Analysis of the Hardware Imprecisions for Scalable and Compact Photonic Tensorized Neural Networks
We simulated tensor-train decomposed neural networks realized by Mach-Zehnder interferometer-based scalable photonic neuromorphic devices. The simulation results demonstrate that under practical hardware imprecisions, the TT-decomposed neural networks can achieve >90% test accuracy with 33.6Ă— fewer MZIs than conventional photonic neural network implementations
Characterizing Coherent Integrated Photonic Neural Networks under Imperfections
Integrated photonic neural networks (IPNNs) are emerging as promising
successors to conventional electronic AI accelerators as they offer substantial
improvements in computing speed and energy efficiency. In particular, coherent
IPNNs use arrays of Mach-Zehnder interferometers (MZIs) for unitary
transformations to perform energy-efficient matrix-vector multiplication.
However, the underlying MZI devices in IPNNs are susceptible to uncertainties
stemming from optical lithographic variations and thermal crosstalk and can
experience imprecisions due to non-uniform MZI insertion loss and quantization
errors due to low-precision encoding in the tuned phase angles. In this paper,
we, for the first time, systematically characterize the impact of such
uncertainties and imprecisions (together referred to as imperfections) in IPNNs
using a bottom-up approach. We show that their impact on IPNN accuracy can vary
widely based on the tuned parameters (e.g., phase angles) of the affected
components, their physical location, and the nature and distribution of the
imperfections. To improve reliability measures, we identify critical IPNN
building blocks that, under imperfections, can lead to catastrophic degradation
in the classification accuracy. We show that under multiple simultaneous
imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even
when the imperfection parameters are restricted within a small range. Our
results also indicate that the inferencing accuracy is sensitive to
imperfections affecting the MZIs in the linear layers next to the input layer
of the IPNN.Comment: This paper has been accepted for publication in the IEEE Journal of
Lightwave Technology (JLT
OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillation
Having the potential for high speed, high throughput, and low energy cost,
optical neural networks (ONNs) have emerged as a promising candidate for
accelerating deep learning tasks. In conventional ONNs, light amplitudes are
modulated at the input and detected at the output. However, the light phases
are still ignored in conventional structures, although they can also carry
information for computing. To address this issue, in this paper, we propose a
framework called OplixNet to compress the areas of ONNs by modulating input
image data into the amplitudes and phase parts of light signals. The input and
output parts of the ONNs are redesigned to make full use of both amplitude and
phase information. Moreover, mutual learning across different ONN structures is
introduced to maintain the accuracy. Experimental results demonstrate that the
proposed framework significantly reduces the areas of ONNs with the accuracy
within an acceptable range. For instance, 75.03% area is reduced with a 0.33%
accuracy decrease on fully connected neural network (FCNN) and 74.88% area is
reduced with a 2.38% accuracy decrease on ResNet-32.Comment: Accepted by Design Automation and Test in Europe (DATE) 202
Photonic spiking neural networks with event-driven femtojoule optoelectronic neurons based on Izhikevich-inspired model
Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput and energy efficiency compared to their electronic neuromorphic counterparts while maintaining their benefits in terms of event-driven computing capability. While state-of-the-art PSNN designs require a continuous laser pump, this paper presents a monolithic optoelectronic PSNN hardware design consisting of an MZI mesh incoherent network and event-driven laser spiking neurons. We designed, prototyped, and experimentally demonstrated this event-driven neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of two photodetectors for excitatory and inhibitory optical spiking inputs, electrical transistors’ circuits providing spiking nonlinearity, and a laser for optical spiking outputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. The nanoscale neuron designed in our monolithic PSNN utilizes quantum impedance conversion. It shows that estimated 21.09 fJ/spike input can trigger the output from on-chip nanolasers running at a maximum of 10 Gspike/second in the neural network. Utilizing the simulated neuron model, we conducted simulations on MNIST handwritten digits recognition using fully connected (FC) and convolutional neural networks (CNN). The simulation results show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. The benchmark shows our PSNN can achieve 50 TOP/J energy efficiency, which corresponds to 100 × throughputs and 1000 × energy-efficiency improvements compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid
- …