Nanotechnology-inspired Information Processing Systems of the Future
- URL: http://arxiv.org/abs/2005.02434v1
- Date: Tue, 5 May 2020 18:52:25 GMT
- Title: Nanotechnology-inspired Information Processing Systems of the Future
- Authors: Randy Bryant, Mark Hill, Tom Kazior, Daniel Lee, Jie Liu, Klara
Nahrstedt, Vijay Narayanan, Jan Rabaey, Hava Siegelmann, Naresh Shanbhag,
Naveen Verma, and H.-S. Philip Wong
- Abstract summary: Nanoscale semiconductor technology has been a key enabler of the computing revolution.
There needs to be a sustained and heavy investment in a nation-wide Vertically Integrated Semiconductor Ecosystem.
A nation-wide VISE provides clear strategic advantages in ensuring the US's global superiority in semiconductors.
- Score: 6.965518964839632
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nanoscale semiconductor technology has been a key enabler of the computing
revolution. It has done so via advances in new materials and manufacturing
processes that resulted in the size of the basic building block of computing
systems - the logic switch and memory devices - being reduced into the
nanoscale regime. Nanotechnology has provided increased computing functionality
per unit volume, energy, and cost. In order for computing systems to continue
to deliver substantial benefits for the foreseeable future to society at large,
it is critical that the very notion of computing be examined in the light of
nanoscale realities. In particular, one needs to ask what it means to compute
when the very building block - the logic switch - no longer exhibits the level
of determinism required by the von Neumann architecture. There needs to be a
sustained and heavy investment in a nation-wide Vertically Integrated
Semiconductor Ecosystem (VISE). VISE is a program in which research and
development is conducted seamlessly across the entire compute stack - from
applications, systems and algorithms, architectures, circuits and nanodevices,
and materials. A nation-wide VISE provides clear strategic advantages in
ensuring the US's global superiority in semiconductors. First, a VISE provides
the highest quality seed-corn for nurturing transformative ideas that are
critically needed today in order for nanotechnology-inspired computing to
flourish. It does so by dramatically opening up new areas of semiconductor
research that are inspired and driven by new application needs. Second, a VISE
creates a very high barrier to entry from foreign competitors because it is
extremely hard to establish, and even harder to duplicate.
Related papers
- Classical Interfaces for Controlling Cryogenic Quantum Computing Technologies [6.667834989995414]
cryogenic quantum systems are among the most mature quantum computing architectures to date.
Recent advancements in control cryoelectronics, both semiconducting and superconducting, are covered.
A view towards newer methods such as optical and wireless qubit interfaces are also presented.
arXiv Detail & Related papers (2025-04-25T17:52:10Z) - Current Opinions on Memristor-Accelerated Machine Learning Hardware [6.670055193544993]
This manuscript reviews the current status of memristor-based machine learning accelerators.
It discusses our opinion on current key challenges that remain in this field, such as device variation, the need for efficient peripheral circuitry, and systematic co-design and optimization.
Memristor-based accelerators could significantly advance the capabilities of AI hardware, particularly for edge applications where power efficiency is paramount.
arXiv Detail & Related papers (2025-01-22T05:10:47Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Coordinated Science Laboratory 70th Anniversary Symposium: The Future of
Computing [80.72844751804166]
In 2021, the Coordinated Science Laboratory CSL hosted the Future of Computing Symposium to celebrate its 70th anniversary.
We summarize the major technological points, insights, and directions that speakers brought forward during the symposium.
Participants discussed topics related to new computing paradigms, technologies, algorithms, behaviors, and research challenges to be expected in the future.
arXiv Detail & Related papers (2022-10-04T17:32:27Z) - Physical Computing for Materials Acceleration Platforms [81.09376948478891]
We argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums.
We outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems.
We expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
arXiv Detail & Related papers (2022-08-17T23:03:54Z) - Future Computer Systems and Networking Research in the Netherlands: A
Manifesto [137.47124933818066]
We draw attention to CompSys as a vital part of ICT.
Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
arXiv Detail & Related papers (2022-05-26T11:02:29Z) - Neurocompositional computing: From the Central Paradox of Cognition to a
new generation of AI systems [120.297940190903]
Recent progress in AI has resulted from the use of limited forms of neurocompositional computing.
New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
arXiv Detail & Related papers (2022-05-02T18:00:10Z) - Advancing Computing's Foundation of US Industry & Society [1.443696537295348]
Underlying IT's impact are the dramatic improvements in computer hardware, which deliver performance that unlock new capabilities.
Will we make the next AI leap without 100x better hardware?
This whitepaper argues for a multipronged effort to develop new computing approaches beyond Moore's Law.
arXiv Detail & Related papers (2021-01-04T23:40:45Z) - Artificial Intelligence at the Edge [25.451110446336276]
5G mobile communication networks increase communication capacity, reduce transmission latency and error, and save energy.
The envisioned future 6G technology will integrate many more technologies, including for example visible light communication.
Many applications require computations and analytics close to application end-points: that is, at the edge of the network, rather than in a centralized cloud.
arXiv Detail & Related papers (2020-12-10T02:08:47Z) - Scaling silicon-based quantum computing using CMOS technology:
State-of-the-art, Challenges and Perspectives [0.0]
We focus on the analysis of the scaling prospects of quantum computing systems based on CMOS technology.
Recent breakthroughs in nanodevice engineering have shown that qubits can now be manufactured in a similar fashion to silicon field-effect transistors.
arXiv Detail & Related papers (2020-11-23T21:59:39Z) - Photonics for artificial intelligence and neuromorphic computing [52.77024349608834]
Photonic integrated circuits have enabled ultrafast artificial neural networks.
Photonic neuromorphic systems offer sub-nanosecond latencies.
These systems could address the growing demand for machine learning and artificial intelligence.
arXiv Detail & Related papers (2020-10-30T21:41:44Z) - Memristors -- from In-memory computing, Deep Learning Acceleration,
Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired
Computing [25.16076541420544]
Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence.
Deep learning has been successfully applied in areas such as object/pattern recognition, speech and natural language processing, self-driving vehicles, intelligent self-diagnostics tools, autonomous robots, knowledgeable personal assistants, and monitoring.
This paper reviews the case for a novel beyond CMOS hardware technology, memristors, as a potential solution for the implementation of power-efficient in-memory computing, deep learning accelerators, and spiking neural networks.
arXiv Detail & Related papers (2020-04-30T16:49:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.