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Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis [article]

Ryan Gary Kim, Janardhan Rao Doppa, Partha Pratim Pande, Diana Marculescu, Radu Marculescu
2017 arXiv   pre-print
Tight collaboration between experts of machine learning and manycore system design is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge.  ...  Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine learning techniques.  ...  Design parameters include: Hierarchy (structure from compute cores to main memory); Distributed or shared memory; cache coherency protocols; Emerging memory technologies.  ... 
arXiv:1712.00076v1 fatcat:rqju4xmpmjetzpbwlnr6x3gbmu

Guest Editorial: IEEE TC Special Issue on Domain-Specific Architectures for Emerging Applications

Lisa Wu Wills, Karthik Swaminathan
2020 IEEE transactions on computers  
Among the published articles, three focused on designing accelerators with post-CMOS technologies such as memristors and other non-volatile technologies, three presented emerging paradigms for design and  ...  Further, the post-Moore landscape has led to accelerators exploring emerging technologies to promise levels of power efficiency that are difficult to attain with existing CMOS-based transistor technologies  ...  In addition to emerging memory technologies and AI, emerging application domains in security, speech, imaging, and aviation all have challenges that require large data volume processing, intensive computations  ... 
doi:10.1109/tc.2020.3002674 fatcat:wkojnwhojjfh3ffh6pcnu563vi

Chip Design 2020

Jaydeep P. Kulkarni
2020 IEEE Micro  
In the second article, Khailany et al. from NVIDIA Corporation present emerging EDA methodologies for accelerating chip design with machine learning.  ...  They discuss various IMC primitives in both CMOS and emerging nonvolatile memory technologies.  ... 
doi:10.1109/mm.2020.3028179 fatcat:mx3dey52rjfdflqumkmmrq6ccu

Arch2030: A Vision of Computer Architecture Research over the Next 15 Years [article]

Luis Ceze, Mark D. Hill, Thomas F. Wenisch
2016 arXiv   pre-print
Application trends, device technologies and the architecture of systems drive progress in information technologies.  ...  More recently, the IEEE Rebooting Computing Initiative explored the future of computing systems in the architecture, device, and circuit domains.  ...  And finally, machine learning has emerged as a key workload; in many respects, machine learning techniques, such as deep learning, caught system designers "by surprise" as an enabler for diverse applications  ... 
arXiv:1612.03182v1 fatcat:d36sywwubnh6dnirw2icyyciom

Affecting reality

Carmen Ng
2019 A Peer-Reviewed Journal About  
This essay emerged from an ongoing project on affective semiotics and social impact game design, in connection with a transnational research project on human-robot interactionsupported by the European  ...  Entanglements among humans, machines, and technologies impact essential issues in the historical present: from surveillance, climate change, cultural heritage, art, to the elicitation, habituation, and  ...  These three facets, from games as designed experiences, ethics of data collection and use for machine learning, to changing research methods, explore on different scales the rising influence of games and  ... 
doi:10.7146/aprja.v8i1.115418 fatcat:r6fnqh2xofdhvpg2jong7ccpjq

Recent Advances in Learning Theory

Weihui Dai, Wlodzislaw Duch, Abdul Hanan Abdullah, Dongrong Xu, Ye-Sho Chen
2015 Computational Intelligence and Neuroscience  
Acknowledgments We are grateful to the editors for hosting this special issue and many thanks are due to the staff for their precious advice during the editorial process of the special issue.  ...  This paper contributed new knowledge to the understanding of EEG signals in the awareness of information security, which is of significance for the development of machine learning technology for the objective  ...  as well as the feature parameters for detecting and analyzing human intrinsic motivation based on machine learning technology in a wearable system.  ... 
doi:10.1155/2015/395948 pmid:26649035 pmcid:PMC4663317 fatcat:3vsdzsb6nvd2nhecbtbqbpsqce

Nanotechnology-inspired Information Processing Systems of the Future [article]

Randy Bryant, Mark Hill, Tom Kazior, Daniel Lee, Jie Liu, Klara Nahrstedt, Vijay Narayanan, Jan Rabaey, Hava Siegelmann, Naresh Shanbhag, Naveen Verma, H.-S. Philip Wong
2020 arXiv   pre-print
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  ...  Emerging advances in nanotechnology will enable tight integration of non-volatile memory [Tighter integration of logic, memory and interconnect: The system hardware designed using these emerging nano-devices  ... 
arXiv:2005.02434v1 fatcat:mzsal23ycfgffoov3ctd5nbfjq

Guest Editorial: Special Issue On Emerging Technologies in Computer Design

Ozgur Sinanoglu, Umit Ogras
2021 IEEE Transactions on Emerging Topics in Computing  
IEEE International Conference on Computer Design (ICCD) invited the highest ranked papers to be included in this special issue of IEEE Transactions on Emerging Technologies in Computing (TETC) in 2017.  ...  for robustness under process variability and radiation; Design techniques for emerging process technologies (MEMs, spintronics, nano, quantum, etc.); Asynchronous circuits; Signal processing, graphic  ...  issue of TETC (January-March 2021): Lorenzo Ferretti, Giovanni Ansaloni and Laura Pozzi, Cluster-Based Heuristic for High Level Synthesis Design Space Exploration Alireza Khodamoradi and Ryan Kastner  ... 
doi:10.1109/tetc.2020.3046058 fatcat:h7e5cwrpgjdevem6isdljiuz6i

COOL CHIPS 2020 Final Program

2020 2020 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)  
It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations.  ...  and the Hardware of Today and Tomorrow Abstract: There is an increasing need to bring machine learning to a wide diversity of hardware devices.  ...  We are rethinking the design of memory cells, computer hardware and software from the scratch.  ... 
doi:10.1109/coolchips49199.2020.9097640 fatcat:gey3ljttvzev7kjognaskbhuf4

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks [article]

Adarsha Balaji and Shihao Song and Twisha Titirsha and Anup Das and Jeffrey Krichmar and Nikil Dutt and James Shackleford and Nagarajan Kandasamy and Francky Catthoor
2021 arXiv   pre-print
We demonstrate the architectural exploration capabilities of NeuroXplorer through case studies with many state-of-the-art machine learning models.  ...  co-design and design-technology co-optimization of the future.  ...  Thus, NeuroXplorer allows to explore the design-space of application performance alongside architecture development.  ... 
arXiv:2105.01795v1 fatcat:yztiegjepvho5ecztv2akaj4vy

A Survey of Machine Learning Applied to Computer Architecture Design [article]

Drew D. Penney, Lizhong Chen
2019 arXiv   pre-print
Recent work, however, has explored broader applicability for design, optimization, and simulation.  ...  Notably, machine learning based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches.  ...  [67] considered both NoC and memory configuration for performance prediction and design space exploration.  ... 
arXiv:1909.12373v1 fatcat:o4nscgkjfbes7kqwmtjvvgl3oa

An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the IoT era

Muhammad Shafique, Theocharis Theocharides, Christos-Savvas Bouganis, Muhammad Abdullah Hanif, Faiq Khalid, Rehan Hafiz, Semeen Rehman
2018 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)  
This paper provides an overview of the current and emerging trends in designing highly efficient, reliable, secure and scalable machine learning architectures for such devices.  ...  Machine learning, and in particular deep learning, is the de facto processing paradigm for intelligently processing these immense volumes of data.  ...  emerging concepts such as self-driving cars, are powered by machine learning technologies.  ... 
doi:10.23919/date.2018.8342120 dblp:conf/date/0001TBHKHR18 fatcat:jb6ouvhtrrevlj3dehuyj6inaq

Emerging Hardware Techniques and EDA Methodologies for Neuromorphic Computing (Dagstuhl Seminar 19152)

Krishnendu Chakrabarty, Tsung-Yi Ho, Hai Li, Ulf Schlichtmann, Michael Wagner
2019 Dagstuhl Reports  
During the seminar, many of the participants presented their current research on the traditional and emerging hardware techniques, design methodologies, electronic design automation techniques, and application  ...  Though interdisciplinary considerations of issues from computer science in the domain of machine learning and large scale computing have already successfully been covered in a series of Dagstuhl seminars  ...  This tool enables design space exploration considering inference accuracy, performance and power tradeoffs.  ... 
doi:10.4230/dagrep.9.4.43 dblp:journals/dagstuhl-reports/ChakrabartyH0S19 fatcat:7fpavhm4gzgxnj2o23jm66sjiy

Emergent Scheduling of Distributed Execution Frameworks

Paul Dean
2019 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)  
training and testing of machine learning models for varying methods of learning, such as, supervised, unsupervised and reinforcement learning [3], [4], exploiting the vast amounts of data available.  ...  Therefore, the projects key problem to be addressed is the exploration of a learning agent capable of learning, identifying and selecting the optimal composition for master and worker nodes at runtime.  ...  Subsequently leading to the final avenue of future work exploring the application of transfer learning techniques to address the problem of a changing state and action space across DEFs, with the motivation  ... 
doi:10.1109/fas-w.2019.00063 dblp:conf/saso/Dean19 fatcat:hjlskgkk5jgrvieg6rnaihmrg4

Applications of Computation-In-Memory Architectures based on Memristive Devices

Said Hamdioui, Hoang Anh Du Nguyen, Mottaqiallah Taouil, Abu Sebastian, Manuel Le Gallo, Sandeep Pande, Siebren Schaafsma, Francky Catthoor, Shidhartha Das, Fernando G. Redondo, G. Karunaratne, Abbas Rahimi (+1 others)
2019 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)  
The applications cover the domains of data analytics, signal processing, and machine learning. *  ...  Therefore, alternative computing architectures are being explored that leverage novel post-CMOS device technologies. One of these is a Computation-in-Memory architecture based on memristive devices.  ...  The emerging new device technologies could play a key role in this exploration.  ... 
doi:10.23919/date.2019.8715020 dblp:conf/date/HamdiouiNTSGPSC19 fatcat:gifssu6pwrhtzhzn757tir26ha
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