Igor Leonidovich Markov | |
---|---|
Ігор Леонідович Марков | |
Born | |
Nationality | Ukraine |
Alma mater | |
Scientific career | |
Fields | Computer Science, Electrical Engineering, Optimization, Electronic Design Automation, Artificial Intelligence, Quantum Computing |
Institutions | University of Michigan, Stanford University, Meta Platforms, Google |
Thesis | Top-Down Timing-Driven Placement with Direct Minimization of Maximal Signal Delay (2001) |
Doctoral advisor | Andrew B. Kahng |
Doctoral students | Smita Krishnaswamy |
Other notable students | Vivek Shende |
Igor Leonidovich Markov [a] (born in 1973) is an American professor, [1] computer scientist and engineer. Markov is known for results in quantum computation, work on limits of computation, research on algorithms for optimizing integrated circuits and on electronic design automation, as well as artificial intelligence. Additionally, Markov is an American non-profit executive [2] responsible for aid to Ukraine worth over a hundred million dollars. [3] [4] [5]
Igor L. Markov has no known relation to the mathematician Andrey Markov.
Markov obtained an M.A. degree in mathematics and a Doctor of Philosophy degree in Computer Science from UCLA in 2001. [6] [7] From the early 2000s through 2018 he was a professor at University of Michigan, [1] where he supervised doctoral dissertations and degrees of 12 students in Electrical engineering and Computer science. [7] He worked as a principal engineer at Synopsys during a sabbatical leave. [8] [9] In 2013-2014 he was a visiting professor at Stanford University. [10] Markov worked at Google on Search and Information Retrieval, [11] and at Meta on Machine Learning platforms. [12] [13] [14] As of 2024, he works at Synopsys. [15]
Markov is a member of the Board of Directors of Nova Ukraine, a California 501(c)(3) charity organization that provides humanitarian aid in Ukraine. [16] At Nova Ukraine, Markov leads government and media relations, as well as advocacy efforts. Markov curated publicity efforts, established and curated large medical and evacuation projects, and contributed to fundraising.
Markov is a member of the Board of Directors of the American Coalition for Ukraine, an umbrella organization that coordinates one hundred US-based nonprofits concerned about events in Ukraine. [17]
ACM Special Interest Group on Design Automation honored Markov with an Outstanding New Faculty Award in 2004. [18]
Markov was the 2009 recipient of IEEE CEDA Ernest S. Kuh Early Career Award "for outstanding contributions to algorithms, methodologies and software for the physical design of integrated circuits." [19] [20] Markov became ACM Distinguished Scientist in 2011. [21] [22] In 2013 he was named an IEEE fellow [23] "for contributions to optimization methods in electronic design automation". [24]
Markov's peer-reviewed scholarly work was recognized with five best-paper awards, including four at major conferences and a journal in the field of electronic design automation, and one in theoretical computer science:
Markov co-authored over 200 peer-reviewed publications in journals and archival conference proceedings, and Google Scholar reported over 19,000 citations of his publications as of October 2023.
In a 2014 Nature article, [37] Markov surveyed known limits to computation, pointing out that many of them are fairly lose and do not restrict near-term technologies. When practical technologies encounter serious limits, understanding these limits can lead to workarounds. More often, what is practically achievable depends on technology-specific engineering limitations.
In 2024, Markov published a paper in Communications of the ACM critical of a prior Nature publication on chip design. [38] [39]
Markov co-edited the two-volume Electronic Design Automation handbook published in second edition by Taylor & Francis in 2016. [40] He also co-authored five scholarly books published by Springer, among them are two textbooks:
Markov's other books cover uncertainty in logic circuits, [44] dealing with functional design errors in digital circuits, [45] and physical synthesis of integrated circuits. [46]
Markov’s contributions include results on quantum circuit synthesis (creating circuits from specifications) and simulation of quantum circuits on conventional computers (obtaining the output of a quantum computer without a quantum computer).
Markov's Capo placer [55] provided a baseline for comparisons used in the placement literature. The placer was commercialized and used to design industry chips. [56] Markov's contributions include algorithms, methodologies and software for
Markov led the development of an end-to-end AI platform called Looper, which supports the full machine learning lifecycle from model training, deployment, and inference all the way to evaluation and tuning of products. Looper provides easy-to-use APIs for optimization, personalization, and feedback collection. [12] [61] [62]
Markov was awarded a Top Writer status on Quora in 2018, 2017, 2016, 2015 and 2014, he has over 80,000 followers. His contributions were republished by Huffington Post , Slate , and Forbes . [63]
Markov is a moderator for the cs.ET (Emerging Technologies in Computing and Communications) subject area on arXiv.
A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. Theoretically a large-scale quantum computer could break some widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.
Shor's algorithm is a quantum algorithm for finding the prime factors of an integer. It was developed in 1994 by the American mathematician Peter Shor. It is one of the few known quantum algorithms with compelling potential applications and strong evidence of superpolynomial speedup compared to best known classical (non-quantum) algorithms. On the other hand, factoring numbers of practical significance requires far more qubits than available in the near future. Another concern is that noise in quantum circuits may undermine results, requiring additional qubits for quantum error correction.
In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. A classical algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. Although all classical algorithms can also be performed on a quantum computer, the term quantum algorithm is generally reserved for algorithms that seem inherently quantum, or use some essential feature of quantum computation such as quantum superposition or quantum entanglement.
Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment. Quantum circuit algorithms can be implemented on integrated circuits, conducted with instrumentation, or written in a programming language for use with a quantum computer or a quantum processor.
Placement is an essential step in electronic design automation — the portion of the physical design flow that assigns exact locations for various circuit components within the chip's core area. An inferior placement assignment will not only affect the chip's performance but might also make it non-manufacturable by producing excessive wire-length, which is beyond available routing resources. Consequently, a placer must perform the assignment while optimizing a number of objectives to ensure that a circuit meets its performance demands. Together, the placement and routing steps of IC design are known as place and route.
Jingsheng Jason Cong is a Chinese-born American computer scientist, educator, and serial entrepreneur. He received his B.S. degree in computer science from Peking University in 1985, his M.S. and Ph.D. degrees in computer science from the University of Illinois at Urbana-Champaign in 1987 and 1990, respectively. He has been on the faculty in the Computer Science Department at the University of California, Los Angeles (UCLA) since 1990. Currently, he is a Distinguished Chancellor's Professor and the director of Center for Domain-Specific Computing (CDSC).
Giovanni De Micheli is a research scientist in electronics and computer science. He is credited for the invention of the Network on a Chip design automation paradigm and for the creation of algorithms and design tools for Electronic Design Automation (EDA). He is Professor and Director of the Integrated Systems laboratory at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Previously, he was Professor of Electrical Engineering at Stanford University. He was Director of the Electrical Engineering Institute at EPFL from 2008 to 2019 and program leader of the Swiss Federal Nano-Tera.ch program. He holds a Nuclear Engineer degree, a M.S. and a Ph.D. degree in Electrical Engineering and Computer Science under Alberto Sangiovanni-Vincentelli.
In electronic design automation, a floorplan of an integrated circuit is a schematic representation of tentative placement of its major functional blocks.
In quantum computing, the threshold theorem states that a quantum computer with a physical error rate below a certain threshold can, through application of quantum error correction schemes, suppress the logical error rate to arbitrarily low levels. This shows that quantum computers can be made fault-tolerant, as an analogue to von Neumann's threshold theorem for classical computation. This result was proven by the groups of Dorit Aharanov and Michael Ben-Or; Emanuel Knill, Raymond Laflamme, and Wojciech Zurek; and Alexei Kitaev independently. These results built on a paper of Peter Shor, which proved a weaker version of the threshold theorem.
John Patrick Hayes is an Irish-American computer scientist and electrical engineer, the Claude E. Shannon Chair of Engineering Science at the University of Michigan. He supervised over 35 doctoral students, coauthored seven books and over 340 peer-reviewed publications. His Erdös number is 2.
Massoud Pedram is an Iranian American computer engineer noted for his research in green computing, energy storage systems, low-power electronics and design, electronic design automation and quantum computing. In the early 1990s, Pedram pioneered an approach to designing VLSI circuits that considered physical effects during logic synthesis. He named this approach layout-driven logic synthesis, which was subsequently called physical synthesis and incorporated into the standard EDA design flows. Pedram's early work on this subject became a significant prior art reference in a litigation between Synopsys Inc. and Magma Design Automation.
Quantum machine learning is the integration of quantum algorithms within machine learning programs.
Andrew MacGregor Childs is an American computer scientist and physicist known for his work on quantum computing. He is currently a professor in the department of computer science and Institute for Advanced Computer Studies at the University of Maryland. He also co-directs the Joint Center for Quantum Information and Computer Science, a partnership between the University of Maryland and the National Institute of Standards and Technology.
Approximate computing is an emerging paradigm for energy-efficient and/or high-performance design. It includes a plethora of computation techniques that return a possibly inaccurate result rather than a guaranteed accurate result, and that can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provide disproportionate gains in performance and energy, while still achieving acceptable result accuracy. For example, in k-means clustering algorithm, allowing only 5% loss in classification accuracy can provide 50 times energy saving compared to the fully accurate classification.
David Atienza Alonso is a Spanish/Swiss scientist in the disciplines of computer and electrical engineering. His research focuses on hardware‐software co‐design and management for energy‐efficient and thermal-aware computing systems, always starting from a system‐level perspective to the actual electronic design. He is a full professor of electrical and computer engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL) and the head of the Embedded Systems Laboratory (ESL). He is an IEEE Fellow (2016), and an ACM Fellow (2022).
In quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. The term was coined by John Preskill in 2012, but the concept dates to Yuri Manin's 1980 and Richard Feynman's 1981 proposals of quantum computing.
Lawrence Pileggi is the Coraluppi Head and Tanoto Professor of Electrical and Computer Engineering at Carnegie Mellon University. He is a specialist in the automation of integrated circuits, and developing software tools for the optimization of power grids. Pileggi's research has been cited thousands of times in engineering papers.
This glossary of quantum computing is a list of definitions of terms and concepts used in quantum computing, its sub-disciplines, and related fields.
Dmitri Maslov is a Canadian-American computer scientist known for his work on quantum circuit synthesis and optimization, quantum advantage, and benchmarking quantum computers. Currently, he is the Chief Software Architect at IBM Quantum. Maslov was formerly a program director for Quantum Information Science at the National Science Foundation. He was named a Fellow of the Institute of Electrical and Electronics Engineers in 2021 "for contributions to quantum circuit synthesis and optimization, and compiling for quantum computers."
Vivek Vijay Shende is an American mathematician known for his work on algebraic geometry, symplectic geometry and quantum computing. He is a professor of Quantum Mathematics at Syddansk Universitet while on leave from University of California Berkeley.