检索结果-内蒙古大学图书馆

Research on General-Purpose brain-inspired computing Systems

Journal of Computer Science & Technology 2024年第1期39卷 4-21页

作者：渠鹏纪兴龙陈嘉杰庞猛李宇晨刘晓义张悠慧 Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Department of Precision Instrument Tsinghua UniversityBeijing 100084China

brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips,basic software and hardware,and algorithms/applications that embody this *** the system is developing rapidly,it faces various challenges and opportunities brought by interdisciplinary research,including the issue of software and hardware *** paper analyzes the status quo of brain-inspired computing *** by some design principle and methodology of general-purpose computers,it is proposed to construct"general-purpose"brain-inspired computing systems.A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware,which can flexibly support various brain-inspired computing applications and neuromorphic chips with different ***,this paper introduces our recent work in these aspects,including the ANN(artificial neural network)/SNN(spiking neural network)development tools,the hardware agnostic compilation infrastructure,and the chip micro-archi-tecture with high flexibility of programming and high performance;these studies show that the"general-purpose"system can remarkably improve the efficiency of application development and enhance the productivity of basic software,thereby being conductive to accelerating the advancement of various brain-inspired algorithms and *** believe that this is the key to the collaborative research and development,and the evolution of applications,basic software and chips in this field,and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.

关键词： brain-inspired computing neuromorphic chip compiler spiking neural network

来源：评论

学校读者我要写书评

暂无评论

brain-inspired computing for Circuit Reliability Characterization

引用

IEEE TRANSACTIONS ON COMPUTERS 2022年第12期71卷 3336-3348页

作者： Genssler, Paul R. R. Amrouch, Hussam Univ Stuttgart Chair Semicond Test & Reliabil STAR Stuttgart Germany

Transistor scaling steadily approaches fundamental limits. Sustaining circuit reliability becomes an overwhelming challenge for foundries and their manufacturing processes. Therefore, early and rapid characterization of degradation effects impacting the circuits' transistors becomes essential. Such degradation effects are caused by design-time variation due to manufacturing variability and/or run-time variation due to transistor aging. In this work, we are the first to employ brain-inspired HDC for circuit reliability. HDC is quickly emerging as an attractive light-weight machine-learning solution. Nowadays, it is mainly applied to bio-signal processing or language recognition. We bring the research of HDC to the next level by demonstrating how it can be applied to address the challenges in circuit reliability. This has far-reaching consequences due to the large savings achieved by 1) reducing the amount of training data and hence the development cycle, 2) removing the need to send the data to the cloud for model training, and 3) speeding up significantly the characterization and classification tasks due to the fast edge-inference. We demonstrate the viability of HDC using SRAM and other circuits as examples. HDC outperforms traditional machine learning methods, such as support vector machine or random forest, in accuracy and requires up to 20x fewer training samples. For a given budget of samples, HDC achieves a 4x smaller error. Our implementation and analysis are based on industrial $14 \;\mathrm{n}\mathrm{m}$14nm FinFET fully calibrated with Intel measurements with respect to both transistor electrical characteristics as well as manufacturing variability. Open Source: Our framework including the algorithm implementation is available for the community to explore other algorithms and circuits at https://***/ML-CAD/HDC-Circuit-Reliability.

关键词： Circuit reliability transistor aging SRAM brain-inspired computing hyperdimensional computing machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-grained system integration for hybrid-paradigm brain-inspired computing

引用

Science China(Information Sciences) 2023年第4期66卷 272-285页

作者： Jing PEI Lei DENG Cheng MA Xue LIU Luping SHI Department of Precision Instrument Center for Brain Inspired Computing Research(CBICR) Beijing Innovation Center for Future Chip Tsinghua University

Hybrid neuromorphic computing supporting the prevailing artificial neural networks and neuroscience-inspired models/algorithms offers substantial flexibility for cross-paradigm model *** is one of the most promising technologies for accelerating intelligence development, ultimately contributing to artificial general intelligence development. Recently, an increasing number of hybrid neuromorphic computing chips have been reported, but such research focuses on chip design without demonstrating systems for large-scale workloads. To this end, we construct a multi-grained system based on many Tianjic chips, presenting a large-scale system for hybrid-paradigm brain-inspired computing. With different numbers of chips and different connection topologies, we develop a Tianjic card and a Tianjic board as the infrastructure for building embedded systems and cloud servers, respectively. Extensive measurements of the communication latency, computational latency, and power consumption evidence the superior potential of Tianjic systems for exploring brain-inspired computing for artificial general intelligence.

关键词： brain-inspired computing multi-grained system integration hybrid paradigm Tianjic chip brain-inspired platform

来源：评论

学校读者我要写书评

暂无评论

Flexibility management and decision making in cyber-physical systems utilizing digital lean principles with brain-inspired computing pattern recognition in Industry 4.0

引用

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第6期37卷 708-725页

作者： Ulhe, Praful P. Dhepe, Aditya D. Shevale, Vaibhav Devidas Warghane, Yash S. Jadhav, Prayag S. Babhare, Success L. JD Coll Engn & Management Nagpur Mech Engn Nagpur Maharashtra India

Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the brain-I

关键词： Cyber-physical systems digital lean CNC machine decision making flexible manufacturing brain-inspired computing

来源：评论

学校读者我要写书评

暂无评论

Emerging Memory Devices Beyond Conventional Data Storage: Paving the Path for Energy-Efficient brain-inspired computing

引用

ELECTROCHEMICAL SOCIETY INTERFACE 2023年第1期32卷 49-51页

作者： Jha, Rashmi Univ Cincinnati Elect Engn & Comp Sci Cincinnati OH 45221 USA

The current state of neuromorphic computing broadly encompasses domain-specific computing architectures designed to accelerate machine learning (ML) and artificial intelligence (AI) algorithms. As is well known, AI/ML algorithms are limited by memory bandwidth. Novel computing architectures are necessary to overcome this limitation. There are several options that are currently under investigation using both mature and emerging memory technologies. For example, mature memory technologies such as high-bandwidth memories (HBMs) are integrated with logic units on the same die to bring memory closer to the computing units. There are also research efforts where in-memory computing architectures have been implemented using DRAMs or flash memory technologies. However, DRAMs suffer from scaling limitations, while flash memory devices suffer from endurance issues. Additionally, in spite of this significant progress, the massive energy consumption needed in neuromorphic processors while meeting the required training and inferencing performance for AI/ML algorithms for future applications needs to be addressed. On the AI/ML algorithm side, there are several pending issues such as life-long learning, explainability, context-based decision making, multimodal association of data, adaptation to address personalized responses, and resiliency. These unresolved challenges in AI/ML have led researchers to explore brain-inspired computing architectures and paradigms.

关键词： neuromorphic computing brain-inspired computing RRAM Devices

来源：评论

学校读者我要写书评

暂无评论

Efficient Off-Policy Reinforcement Learning via brain-inspired computing 23

Efficient Off-Policy Reinforcement Learning via Brain-Inspir...

引用

33rd Great Lakes Symposium on VLSI (GLSVLSI)

作者： Ni, Yang Abraham, Danny Issa, Mariam Kim, Yeseong Mercati, Pietro Imani, Mohsen Univ Calif Irvine Irvine CA 92717 USA Daegu Gyeongbuk Inst Sci & Technol Daegu South Korea Intel Labs Hillsboro OR USA

ISBN: (纸本)9798400701252

Reinforcement Learning (RL) has opened up new opportunities to enhance existing smart systems that generally include a complex decision-making process. However, modern RL algorithms, e.g., Deep Q-Networks (DQN), are based on deep neural networks, resulting in high computational costs. In this paper, we propose QHD, an off-policy value-based Hyperdimensional Reinforcement Learning, that mimics brain properties toward robust and realtime learning. QHD relies on a lightweight brain-inspired model to learn an optimal policy in an unknown environment. On both desktop and power-limited embedded platforms, QHD achieves signiffcantly better overall effciency than DQN while providing higher or comparable rewards. QHD is also suitable for highlyeffcient reinforcement learning with great potential for online and real-time learning. Our solution supports a small experience replay batch size that provides 12.3x speedup compared to DQN while ensuring minimal quality loss. Our evaluation shows QHD capability for real-time learning, providing 34.6x speedup and signiffcantly better quality of learning than DQN.

关键词： Hyperdimensional computing brain-inspired computing

来源：评论

学校读者我要写书评

暂无评论

A review of basic software for brain-inspired computing

引用

CCF TRANSACTIONS ON HIGH PERFORMANCE computing 2022年第1期4卷 34-42页

作者： Qu, Peng Yang, Le Zheng, Weimin Zhang, Youhui Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Comp Sci & Technol Beijing Peoples R China State Key Lab Math Engn & Adv Comp Wuxi Jiangsu Peoples R China

brain-inspired computing, which is inspired by the information processing procedure and the biophysiological structure of the brain, is believed to have the potential to drive the next wave of computer engineering and provide a promising way for the next generation of artificial intelligence. The basic software for brain-inspired computing is the core link to realize the research goals of brain-inspired computing and build the ecological environment of brain-inspired computing applications. This paper reviews the status of the three major kinds of basic software for brain-inspired computing. Namely, the toolchains for neuromorphic chips, the software simulation frameworks, and the frameworks that integrate spiking neural networks (SNNs) and deep neural networks (DNNs). Afterward, we point out that a "general-purpose" hierarchical and HW/SW decoupled basic software framework would be beneficial to both the (computational) neuroscience and brain-inspired intelligence fields. And the notion "general-purpose" refers to the decoupling of software and hardware and supports the integration of computer science and neuroscience related research.

关键词： brain-inspired computing Basic software Neuromorphic toolchains SNN simulation Decoupling software and hardware

来源：评论

学校读者我要写书评

暂无评论

Advancing brain-inspired computing with hybrid neural networks

引用

National Science Review 2024年第5期11卷 56-71页

作者： Faqiang Liu Hao Zheng Songchen Ma Weihao Zhang Xue Liu Yansong Chua Luping Shi Rong Zhao Center for Brain-Inspired Computing Research Optical Memory National Engineering Research CenterTsinghua University-China Electronics Technology HIK Group Co.Joint Research Center for Brain-inspired ComputingIDGMc Govern Institute for Brain Research Department of Precision Instrument Tsinghua University Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information Technology

brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network(HNN), which integrates computer-science-oriented artificial neural networks(ANNs) with neuroscience-oriented spiking neural networks(SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

关键词： brain-inspired computing hybrid neural network dual-brain driven multi-network integration neuromorphic system

来源：评论

学校读者我要写书评

暂无评论

Towards“General Purpose”brain-inspired computing System

引用

Tsinghua Science and Technology 2021年第5期26卷 664-673页

作者： Youhui Zhang Peng Qu Weimin Zheng the Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

brain-inspired computing refers to computational models,methods,and systems,that are mainly inspired by the processing mode or structure of brain.A recent study proposed the concept of"neuromorphic completeness"and the corresponding system hierarchy,which is helpful to determine the capability boundary of brain-inspired computing system and to judge whether hardware and software of brain-inspired computing are compatible with each *** a position paper,this article analyzes the existing brain-inspired chips design characteristics and the current so-called"general purpose"application development frameworks for brain-inspired computing,as well as introduces the background and the potential of this ***,some key features of this concept are presented through the comparison with the Turing completeness and approximate computation,and the analyses of the relationship with"general-purpose"brain-inspired computing systems(it means that computing systems can support all computable applications).In the end,a promising technical approach to realize such computing systems is introduced,as well as the on-going research and the work *** believe that this work is conducive to the design of extensible neuromorphic complete hardware-primitives and the corresponding *** this basis,it is expected to gradually realize"general purpose"brain-inspired computing system,in order to take into account the functionality completeness and application efficiency.

关键词： brain-inspired computing neuromorphic computing computational completeness hardware/software decoupling system hierarchy

来源：评论

学校读者我要写书评

暂无评论

DeepSeek or ChatGPT: Can brain-computer interfaces/brain-inspired computing achieve leapfrog development with large AI models?

Brain-X

引用

brain-X 2025年第1期3卷

作者： Bai, Long Chen, Shugeng Wang, Peng Chen, He Yan, Jiaqing Zhu, Xiaojian Song, Enming Tian, Bobo Su, Jiacan Li, Xiaoli Organoid Research Center Institute of Translational Medicine Shanghai University Shanghai China Department of Orthopedics Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China National Center for Translational Medicine (Shanghai) SHU Branch Shanghai University Shanghai China Wenzhou Institute of Shanghai University Zhejiang Wenzhou China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Department of Language Literature and Communication Faculty of Humanities Vrije Universiteit Amsterdam Amsterdam Netherlands School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou China Pazhou Laboratory Guangdong Guangzhou China School of Electrical and Control Engineering North China University of Technology Beijing China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo China Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Key Laboratory of Polar Materials and Devices Department of Electronics Ministry of Education East China Normal University Shanghai China

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：