19 research outputs found
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. Previous ONN architectures are mainly designed for
general matrix multiplication (GEMM), leading to unnecessarily large area cost
and high control complexity. Here, we move beyond classical GEMM-based ONNs and
propose an optical subspace neural network (OSNN) architecture, which trades
the universality of weight representation for lower optical component usage,
area cost, and energy consumption. We devise a butterfly-style
photonic-electronic neural chip to implement our OSNN with up to 7x fewer
trainable optical components compared to GEMM-based ONNs. Additionally, a
hardware-aware training framework is provided to minimize the required device
programming precision, lessen the chip area, and boost the noise robustness. We
experimentally demonstrate the utility of our neural chip in practical image
recognition tasks, showing that a measured accuracy of 94.16% can be achieved
in hand-written digit recognition tasks with 3-bit weight programming
precision.Comment: 17 pages,5 figure
Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu
Efficient Online Processing with Deep Neural Networks
The capabilities and adoption of deep neural networks (DNNs) grow at an
exhilarating pace: Vision models accurately classify human actions in videos
and identify cancerous tissue in medical scans as precisely than human experts;
large language models answer wide-ranging questions, generate code, and write
prose, becoming the topic of everyday dinner-table conversations. Even though
their uses are exhilarating, the continually increasing model sizes and
computational complexities have a dark side. The economic cost and negative
environmental externalities of training and serving models is in evident
disharmony with financial viability and climate action goals.
Instead of pursuing yet another increase in predictive performance, this
dissertation is dedicated to the improvement of neural network efficiency.
Specifically, a core contribution addresses the efficiency aspects during
online inference. Here, the concept of Continual Inference Networks (CINs) is
proposed and explored across four publications. CINs extend prior
state-of-the-art methods developed for offline processing of spatio-temporal
data and reuse their pre-trained weights, improving their online processing
efficiency by an order of magnitude. These advances are attained through a
bottom-up computational reorganization and judicious architectural
modifications. The benefit to online inference is demonstrated by reformulating
several widely used network architectures into CINs, including 3D CNNs,
ST-GCNs, and Transformer Encoders. An orthogonal contribution tackles the
concurrent adaptation and computational acceleration of a large source model
into multiple lightweight derived models. Drawing on fusible adapter networks
and structured pruning, Structured Pruning Adapters achieve superior predictive
accuracy under aggressive pruning using significantly fewer learned weights
compared to fine-tuning with pruning.Comment: PhD Dissertatio
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy
Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems
The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems
The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system
SpiNNaker - A Spiking Neural Network Architecture
20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come