Photonic-electronic integrated circuits for high-performance computing and ai accelerators
In recent decades, the demand for computational power has surged, particularly with the
rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, the …
rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, the …
Light in ai: toward efficient neurocomputing with optical neural networks—a tutorial
In the post Moore's era, conventional electronic digital computing platforms have
encountered escalating challenges to support massively parallel and energy-hungry …
encountered escalating challenges to support massively parallel and energy-hungry …
A compact butterfly-style silicon photonic–electronic neural chip for hardware-efficient deep learning
The optical neural network (ONN) is a promising hardware platform for next-generation
neurocomputing due to its high parallelism, low latency, and low energy consumption …
neurocomputing due to its high parallelism, low latency, and low energy consumption …
L2ight: Enabling on-chip learning for optical neural networks via efficient in-situ subspace optimization
Silicon-photonics-based optical neural network (ONN) is a promising hardware platform that
could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility, ultra …
could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility, ultra …
SqueezeLight: Towards scalable optical neural networks with multi-operand ring resonators
Optical neural networks (ONNs) have demonstrated promising potentials for next-generation
artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy …
artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy …
Parity–time symmetric optical neural networks
H Deng, M Khajavikhan - Optica, 2021 - opg.optica.org
Optical neural networks (ONNs), implemented on an array of cascaded Mach–Zehnder
interferometers (MZIs), have recently been proposed as a possible replacement for …
interferometers (MZIs), have recently been proposed as a possible replacement for …
Tensor-compressed back-propagation-free training for (physics-informed) neural networks
Backward propagation (BP) is widely used to compute the gradients in neural network
training. However, it is hard to implement BP on edge devices due to the lack of hardware …
training. However, it is hard to implement BP on edge devices due to the lack of hardware …
Toward hardware-efficient optical neural networks: Beyond FFT architecture via joint learnability
As a promising neuromorphic framework, the optical neural network (ONN) demonstrates
ultrahigh inference speed with low energy consumption. However, the previous ONN …
ultrahigh inference speed with low energy consumption. However, the previous ONN …
Efficient on-chip learning for optical neural networks through power-aware sparse zeroth-order optimization
Optical neural networks (ONNs) have demonstrated record-breaking potential in high-
performance neuromorphic computing due to their ultra-high execution speed and low …
performance neuromorphic computing due to their ultra-high execution speed and low …
Scatter: Algorithm-circuit co-sparse photonic accelerator with thermal-tolerant, power-efficient in-situ light redistribution
Photonic computing has emerged as a promising solution for accelerating computation-
intensive artificial intelligence (AI) workloads. However, limited reconfigurability, high …
intensive artificial intelligence (AI) workloads. However, limited reconfigurability, high …