62 research outputs found
Large-Scale Optical Neural Networks based on Photoelectric Multiplication
Recent success in deep neural networks has generated strong interest in
hardware accelerators to improve speed and energy consumption. This paper
presents a new type of photonic accelerator based on coherent detection that is
scalable to large () networks and can be operated at high (GHz)
speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using
the massive spatial multiplexing enabled by standard free-space optical
components. In contrast to previous approaches, both weights and inputs are
optically encoded so that the network can be reprogrammed and trained on the
fly. Simulations of the network using models for digit- and
image-classification reveal a "standard quantum limit" for optical neural
networks, set by photodetector shot noise. This bound, which can be as low as
50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for
digital irreversible computation is theoretically possible in this device. The
proposed accelerator can implement both fully-connected and convolutional
networks. We also present a scheme for back-propagation and training that can
be performed in the same hardware. This architecture will enable a new class of
ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5,
figures, 2 table
Design of Reconfigurable On-Chip Optical Architectures based on Phase Change Material
Integrated optics is a promising technology to take the advantage of light propagation for high throughput chip-scale computing architectures and interconnects. Optical devices call for reconfigurable architectures to maximize resource utilization. Typical reconfigurable optical computing architectures involve micro-ring resonators for electro-optic modulation. However, such devices require voltage and thermal tuning to compensate for fabrication process variability and thermal sensitivity. To tackle this challenge we propose to use non-volatile Phase Change Material (PCM) to configure optical path. The non-volatility of PCM elements allows maintaining the optical path without consuming energy and the high contrast between two state of crystalline (cr) and amorphous (am) allows to route signal only through the required resonators, thus saving the calibration energy of bypassed resonators. We evaluate the efficiency of PCM based design on Reconfigurable Directed Logic (RDL) and nanophotonic interconnect. We develop a model allowing to estimate optical and electrical energy consumption. In the context of nanophotonic interconnect we evaluate the efficiency of the proposed PCM-based interconnects using system level simulations carried out with SNIPER manycore simulator. Results show that the proposed implementation allows reducing the static power by 53% on average for RDL and communication power saving up to 52% is achieved for nanophotonic interconnect
Design of Stochastic Computing Architectures using Integrated Optics
Approximate computing (AC) is an emerging computing approach that allows to trade off design energy efficiency with computing accuracy. It targets error resilient applications, such as image processing, where energy consumption is of major concern. Stochastic computing (SC) is an approximate computing paradigm that leads to energy efficient and reduced hardware complexity designs. In this approach, data is represented as probabilities in bit streams format. The main drawback of this computing paradigm is the intrinsic serial processing of bit streams, which negatively impacts the processing time. Nanophotonics technology is characterized by high bandwidth and high signals propagation speed, which has the potential to support the electrical domain in computations to speed up the processing rate. The major issues in optical computing (OC) remain the large size of silicon photonics devices, which impact the design scalability. In this thesis, we propose, for the first time, an optical stochastic computing (OSC) approach, where we aim to design SC architectures using integrated optics. For this purpose, we propose a methodology that has libraries for optical processing and interfaces, e.g., bit stream generator. We design all-optical gates for the computation and develop transmission models for the architectures. The methodology allows for design space exploration of technological and system-level parameters to optimize design performance, i.e., energy efficiency, computing accuracy, and latency, for the targeted application. This exploration leads to multiple design options that satisfy different design requirements for the selected application.
The optical processing libraries include designing a polynomial architecture that can execute any arbitrary single input function. We explore the design parameters by implementing a Gamma correction application for image processing. Results show a 4.5x increase in the errors, which leads to 47x energy saving and 16x faster processing speed. We propose a reconfigurable polynomial architecture to adapt design order at run-time. The design allows the execution of high order polynomial functions for better accuracy or multiple low order functions to increase throughput and energy efficiency. Finally, we propose the design of combinational filters. The purpose is to investigate the design of cascaded gates architectures using photonic crystal (PhC) nanocavities. We use this device to design a Sobel edge detection filter for image processing. The resulting architecture shows 0.85nJ/pixel energy consumption and 51.2ns/pixel processing time. The optical interface libraries include designing different architectures of stochastic number generators (SNG) that are either electrical-optical or all-optical to generate the bit streams. We compare these SNGs in terms of computing accuracy and energy efficiency. The results show that all implementations can lead to the same level of computing accuracy. Moreover, using an all-optical SNG to design a fully optical 8-bit adder results in 98% reduction in hardware complexity and 70% energy saving compared to a conventional optical design
Quantum Computing for Space: Exploring Quantum Circuits on Programmable Nanophotonic Chips
Quantum circuits are the fundamental computing model of quantum computing. It consists of a sequence of quantum gates that act on a set of qubits to perform a specific computation. For the implementation of quantum circuits, programmable nanophotonic chips provide a promising foundation with a large number of qubits. The current study explores the possible potential of quantum circuits implemented on programmable nanophotonic chips for space technology. In the recent findings, it has been demonstrated that quantum circuits have several advantages over classical circuits, such as exponential speedups, multiple parallel computations, and compact size. Apart from this, nanophotonic chips also offer a number of advantages over traditional chips. They provide high-speed data transfer as light travels faster than electrons. Photons require less energy to transmit data than electrons, so nanophotonic chips consume less power than conventional chips. The bandwidth of nanophotonic chips is greater than that of traditional chips, so they can transfer more data simultaneously. They can be easily scaled to smaller sizes with higher densities and are more robust to extreme temperatures and radiation than classical chips. The focus of the current study is on how quantum circuits could revolutionize space technology by providing faster and more efficient computations for a variety of space-related applications. All the in-depth analysis is carried out while taking currently available state-of-the-art technologies into consideration
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