From Artificial Intelligence to Brain Intelligence
The field of AI is not new to researchers, as its foundations were established in the 1950s. After many decades of inattention, there has been a dramatic resurgence of interest in AI, fueled by a confluence of several factors. The benefits of decades of Dennard scaling and Moore's law miniaturi...
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2020
Routledge |
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| ISBN: | 9788770221238, 8770221235 |
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| Abstract | The field of AI is not new to researchers, as its foundations were established in the 1950s. After many decades of inattention, there has been a dramatic resurgence of interest in AI, fueled by a confluence of several factors. The benefits of decades of Dennard scaling and Moore's law miniaturization, coupled with the rise of highly distributed processing, have led to massively parallel systems well suited for handling big data. The widespread availability of big data, necessary for training AI algorithms, is another important factor. Finally, the greatly increased compute power and memory bandwidths have enabled deeper networks and new algorithms capable of accuracy rivaling that of human perception. Already AI has shown success in many diverse areas, including finance (portfolio management, investment strategies), marketing, health care, transportation, gaming, defense, robotics, computer vision, education, search engines, online assistants, image/facial recognition, anomaly detection, spam filtering, online customer service, biometric sensors, and predictive maintenance, to name a few. Despite these remarkable advances, the human brain is still superior in many ways - including, notably, energy efficiency and one-shot learning - giving researchers new areas to explore. In summary, AI research and applications will continue with vigor in software, algorithms, and hardware accelerators. These exciting developments have also brought new questions of ethics and privacy, areas which must be studied in tandem with technological advances. To continue the success story of AI, the AI Compute symposium was launched with the sponsorship of IBM, IEEE CAS and EDS for the first time. The aim of this publication is to compile all the materials presented by the renowned speakers in the symposium into a book format, serving as a learning tool for the audience. This book contains two broad topics: general AI advances (chapters 1-5) and neuromorphic computing directions (chapters 6-9). Technical topics discussed in the book include: Research Directions in AI algorithms and systems An ARM perspective on hardware requirements and challenges for AI The new Era of AI hardware AI and the Opportunity for Unconventional Computing Platforms Thermodynamic Computing Brain-like cognitive engineering system BRAINWAY and Nano - Abacus architecture: Brain-inspired Cognitive Computing using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design Applying Lessons from Nature for Today's Computing Challenges Emerging Memories - RRAM Fabric for Neuromorphic Computing Applications |
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| AbstractList | The field of AI is not new to researchers, as its foundations were established in the 1950s. After many decades of inattention, there has been a dramatic resurgence of interest in AI, fueled by a confluence of several factors. The benefits of decades of Dennard scaling and Moore's law miniaturization, coupled with the rise of highly distributed processing, have led to massively parallel systems well suited for handling big data. The widespread availability of big data, necessary for training AI algorithms, is another important factor. Finally, the greatly increased compute power and memory bandwidths have enabled deeper networks and new algorithms capable of accuracy rivaling that of human perception. Already AI has shown success in many diverse areas, including finance (portfolio management, investment strategies), marketing, health care, transportation, gaming, defense, robotics, computer vision, education, search engines, online assistants, image/facial recognition, anomaly detection, spam filtering, online customer service, biometric sensors, and predictive maintenance, to name a few. The field of AI is not new to researchers, as its foundations were established in the 1950s. After many decades of inattention, there has been a dramatic resurgence of interest in AI, fueled by a confluence of several factors. The benefits of decades of Dennard scaling and Moore's law miniaturization, coupled with the rise of highly distributed processing, have led to massively parallel systems well suited for handling big data. The widespread availability of big data, necessary for training AI algorithms, is another important factor. Finally, the greatly increased compute power and memory bandwidths have enabled deeper networks and new algorithms capable of accuracy rivaling that of human perception. Already AI has shown success in many diverse areas, including finance (portfolio management, investment strategies), marketing, health care, transportation, gaming, defense, robotics, computer vision, education, search engines, online assistants, image/facial recognition, anomaly detection, spam filtering, online customer service, biometric sensors, and predictive maintenance, to name a few. Despite these remarkable advances, the human brain is still superior in many ways - including, notably, energy efficiency and one-shot learning - giving researchers new areas to explore. In summary, AI research and applications will continue with vigor in software, algorithms, and hardware accelerators. These exciting developments have also brought new questions of ethics and privacy, areas which must be studied in tandem with technological advances. To continue the success story of AI, the AI Compute symposium was launched with the sponsorship of IBM, IEEE CAS and EDS for the first time. The aim of this publication is to compile all the materials presented by the renowned speakers in the symposium into a book format, serving as a learning tool for the audience. This book contains two broad topics: general AI advances (chapters 1-5) and neuromorphic computing directions (chapters 6-9). Technical topics discussed in the book include: Research Directions in AI algorithms and systems An ARM perspective on hardware requirements and challenges for AI The new Era of AI hardware AI and the Opportunity for Unconventional Computing Platforms Thermodynamic Computing Brain-like cognitive engineering system BRAINWAY and Nano - Abacus architecture: Brain-inspired Cognitive Computing using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design Applying Lessons from Nature for Today's Computing Challenges Emerging Memories - RRAM Fabric for Neuromorphic Computing Applications Research in Artificial Intelligence (AI) is not new, it has been around since 1950s. AI resurfaced at that time while Moores law was on an aggressive path of scaling, with the transformation of NMOS and later bipolar technology to CMOS for high performance, low power as well as low cost applications.Several breakthroughs in the electronics industry helped to push Moores law in chip miniaturization along with increased computing power (parallel and distributed processing) and memory bandwidth. Once this paradigm shift occurred it naturally opened doors for AI as it required big data manipulations, and thus AI could thrive again. AI has already shown success in industries such as finance, marketing, health care, transportation, gaming, education and the defence and space, to name but a few.The human brain amazingly has a memory in the order of millions of digital bits, however it cannot compete with machines for data crunching and speed. Thus tomorrows world will be a World of Wonders of Artificial Intelligence (WOW- AI), to compensate the computational limitations of human beings. In short, AI research and applications will continue to grow with the development of software, algorithms and hardware accelerators.To continue the development of AI, an advanced AI Compute Symposium was launched with the sponsorship of IBM, IEEE CAS and EDS, from which this book came. Overall, the book covers two broad topics: general AI advances, and applications to neuromorphic computing. |
| Author | Joshi, Rajiv |
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| Contributor | Ziegler, Matthew Alarcon, Eduard Kumar, Arvind |
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| Copyright | 2020 River Publishers 2020 |
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| DOI | 10.1201/9781003338215 |
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| Editor | Ziegler, Matthew Joshi, Rajiv Alarcon, Eduard Kumar, Arvind |
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| Keywords | Synaptic Lasso Artificial intelligence Congresses RRAM Deep Neural Networks Ai Algorithm IoT Intelligence artificielle Congrès RRAM Device SNR Training Error Sparse Coding In-memory Computation NN Model Keyword Spotting Synaptic Plasticity Exact Bayesian Inference Anniversary Vector Matrix Multiplication AI Research IBM Research Neuromorphic Computing IEEE Circuit In-memory Computing Artificial intelligence Thermodynamic Computing Arm RC System |
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| Snippet | The field of AI is not new to researchers, as its foundations were established in the 1950s. After many decades of inattention, there has been a dramatic... Research in Artificial Intelligence (AI) is not new, it has been around since 1950s. AI resurfaced at that time while Moores law was on an aggressive path of... |
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| TableOfContents | Introduction 7 1. Research Directions in AI Algorithms and Systems 11 2. An Arm Perspective on Hardware Requirements and Challenges for AI 27 3. The New Era of AI Hardware 55 4. AI and the Opportunity for Unconventional Computing Platforms 67 5. Thermodynamic Computing 85 6. Brain-like Cognitive Engineering Systems 101 7. BRAINWAY and nano-Abacus Architecture: Brain-inspired Cognitive Computing Using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design 135 8. The Loihi Neuromorphic Research Chip 161 9. RRAM Fabric for Neuromorphic Computing Applications 175 About the Editors 191 About the Authors 199 Title Page Introduction Table of Contents 1. Research Directions in AI Algorithms and Systems 2. An ARM Perspective on Hardware Requirements and Challenges for AI 3. The New Era of AI Hardware 4. AI and the Opportunity for Unconventional Computing Platforms 5. Thermodynamic Computing 6. Brain-Like Cognitive Engineering Systems 7. BRAINWAY and Nano-Abacus Architecture: Brain-Inspired Cognitive Computing Using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design 8. The Loihi Neuromorphic Research Chip 9. RRAM Fabric for Neuromorphic Computing Applications About the Editors About the Authors |
| Title | From Artificial Intelligence to Brain Intelligence |
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