76 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
ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores
Photonic tensor cores (PTCs) are essential building blocks for optical
artificial intelligence (AI) accelerators based on programmable photonic
integrated circuits. PTCs can achieve ultra-fast and efficient tensor
operations for neural network (NN) acceleration. Current PTC designs are either
manually constructed or based on matrix decomposition theory, which lacks the
adaptability to meet various hardware constraints and device specifications. To
our best knowledge, automatic PTC design methodology is still unexplored. It
will be promising to move beyond the manual design paradigm and "nurture"
photonic neurocomputing with AI and design automation. Therefore, in this work,
for the first time, we propose a fully differentiable framework, dubbed ADEPT,
that can efficiently search PTC designs adaptive to various circuit footprint
constraints and foundry PDKs. Extensive experiments show superior flexibility
and effectiveness of the proposed ADEPT framework to explore a large PTC design
space. On various NN models and benchmarks, our searched PTC topology
outperforms prior manually-designed structures with competitive matrix
representability, 2-30x higher footprint compactness, and better noise
robustness, demonstrating a new paradigm in photonic neural chip design. The
code of ADEPT is available at https://github.com/JeremieMelo/ADEPT using the
https://github.com/JeremieMelo/pytorch-onn (TorchONN) library.Comment: Accepted to ACM/IEEE Design Automation Conference (DAC), 202
DOTA: A Dynamically-Operated Photonic Tensor Core for Energy-Efficient Transformer Accelerator
The wide adoption and significant computing resource consumption of
attention-based Transformers, e.g., Vision Transformer and large language
models, have driven the demands for efficient hardware accelerators. While
electronic accelerators have been commonly used, there is a growing interest in
exploring photonics as an alternative technology due to its high energy
efficiency and ultra-fast processing speed. Optical neural networks (ONNs) have
demonstrated promising results for convolutional neural network (CNN) workloads
that only require weight-static linear operations. However, they fail to
efficiently support Transformer architectures with attention operations due to
the lack of ability to process dynamic full-range tensor multiplication. In
this work, we propose a customized high-performance and energy-efficient
photonic Transformer accelerator, DOTA. To overcome the fundamental limitation
of existing ONNs, we introduce a novel photonic tensor core, consisting of a
crossbar array of interference-based optical vector dot-product engines, that
supports highly-parallel, dynamic, and full-range matrix-matrix multiplication.
Our comprehensive evaluation demonstrates that DOTA achieves a >4x energy and a
>10x latency reduction compared to prior photonic accelerators, and delivers
over 20x energy reduction and 2 to 3 orders of magnitude lower latency compared
to the electronic Transformer accelerator. Our work highlights the immense
potential of photonic computing for efficient hardware accelerators,
particularly for advanced machine learning workloads.Comment: The short version is accepted by Next-Gen AI System Workshop at MLSys
202
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
([email protected]
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
N/S Co-doped Carbon Derived From Cotton as High Performance Anode Materials for Lithium Ion Batteries
Highly porous carbon with large surface areas is prepared using cotton as carbon sources which derived from discard cotton balls. Subsequently, the sulfur-nitrogen co-doped carbon was obtained by heat treatment the carbon in presence of thiourea and evaluated as Lithium-ion batteries anode. Benefiting from the S, N co-doping, the obtained S, N co-doped carbon exhibits excellent electrochemical performance. As a result, the as-prepared S, N co-doped carbon can deliver a high reversible capacity of 1,101.1 mA h g−1 after 150 cycles at 0.2 A g−1, and a high capacity of 531.2 mA h g−1 can be observed even after 5,000 cycles at 10.0 A g−1. Moreover, excellently rate capability also can be observed, a high capacity of 689 mA h g−1 can be obtained at 5.0 A g−1. This superior lithium storage performance of S, N co-doped carbon make it as a promising low-cost and sustainable anode for high performance lithium ion batteries
The Control Technology Research of the Z-source Three-phase Four-bridge Arm Inverter
Z-source inverter can boost the voltage of the DC-side, allow the two switches of the same bridge arm conducting at the same time and it has some other advantages. The zero-sequence current flows through the fourth leg of the three-phase four-leg inverter so the three-phase four-leg inverter can work with unbalanced load. This paper presents a Z-source three-phase four-leg inverter which combines a Z-source network with three-phase four-leg inverter. The circuit uses simple SPWM modulation technique and the fourth bridge arm uses fully compensated control method. The inverter can maintain a symmetrical output voltage when the proposed scheme under the unbalanced load
Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews
Online course reviews have been an essential way in which course providers could get insights into students’ perceptions about the course quality, especially in the context of massive open online courses (MOOCs), where it is hard for both parties to get further interaction. Analyzing online course reviews is thus an inevitable part for course providers towards the improvement of course quality and the structuring of future courses. However, reading through the often-time thousands of comments and extracting key ideas is not efficient and will potentially incur non-coverage of some important ideas. In this work, we propose a key idea extractor that is based on fine-grained aspect-level semantic units from comments, powered by different variations of state-of-the-art pre-trained language models (PLMs). Our approach differs from both previous topic modeling and keyword extraction methods, which lies in: First, we aim to not only eliminate the heavy reliance on human intervention and statistical characteristics that traditional topic models like LDA are based on, but also to overcome the coarse granularity of state-of-the-art topic models like top2vec. Second, different from previous keyword extraction methods, we do not extract keywords to summarize each comment, which we argue is not necessarily helpful for human readers to grasp key ideas at the course level. Instead, we cluster the ideas and concerns that have been most expressed throughout the whole course, without relying on the verbatimness of students’ wording. We show that this method provides high and stable coverage of students’ ideas
- …