Massoud Pedram

Bio: Massoud Pedram is an academic researcher from University of Southern California. The author has contributed to research in topics: Energy consumption & CMOS. The author has an hindex of 77, co-authored 780 publications receiving 23047 citations. Previous affiliations of Massoud Pedram include University of California, Berkeley & Syracuse University.

Squash: a scalable quantum mapper considering ancilla sharing

Abstract: Quantum algorithms for solving problems of interesting size often result in circuits with a very large number of qubits and quantum gates. Fortunately, these algorithms also tend to contain a small number of repetitively-used quantum kernels. Identifying the quantum logic blocks that implement such quantum kernels is critical to the complexity management for realizing the corresponding quantum circuit. Moreover, quantum computation requires some type of quantum error correction coding to combat decoherence, which in turn results in a large number of ancilla qubits in the circuit. Sharing the ancilla qubits among quantum operations (even though this sharing can increase the overall circuit latency) is important in order to curb the resource demand of the quantum algorithm. This paper presents a multi-core reconfigurable quantum processor architecture, called Requp, which supports a layered approach to mapping a quantum algorithm and ancilla sharing. More precisely, a scalable quantum mapper, called Squash, is introduced, which divides a given quantum circuit into a number of quantum kernels--each kernel comprises k parts such that each part will run on exactly one of k available cores. Experimental results demonstrate that Squash can handle large-scale quantum algorithms while providing an effective mechanism for sharing ancilla qubits.

qGDR: A Via-Minimization-Oriented Routing Tool for Large-Scale Superconductive Single-Flux-Quantum Circuits

Abstract: Single-flux-quantum (SFQ) circuit technologies are promising digital circuit technologies with high-speed and extremely low-power characteristics. However, heavy wire routing tasks are finished either by considerable human effort or by commercial routing tools with few physical considerations for the SFQ circuits. In this paper, we present an integrated global and detailed router for the SFQ circuits, qGDR, which aims at reducing the impedance mismatch during signal transfer by minimizing the total number of used vias. The global router allocates routing resources while minimizing the via usage by a dynamic layer assignment algorithm. The detailed router follows the global routing results to complete the routing task by resorting to a maze routing algorithm. Following the MIT-LL SFQ5ee process technology, qGDR can use only two routing layers to route an 8-bit integer divider with more than 40 000 Josephson junctions in less than one hour.

Sensitivity-based gate delay propagation in static timing analysis

Abstract: The paper presents a methodology for accurate propagation of delay information through a gate for the purpose of static timing analysis (STA) in the presence of noise. Conventional STA tools represent an electrical waveform at the intermediate node of a logic circuit by its arrival time and slope. In general, these two parameters are calculated based on the time instances at which the input waveform passes through predetermined voltage levels. However, to account properly for the impact of noise on the shape of a waveform, it is insufficient to model the waveform using only two parameters. The key contribution of the proposed methodology is to base the timing analysis on the sensitivity of the output waveform to the input waveform and accurately, yet efficiently, propagate equivalent electrical waveforms throughout a VLSI circuit. A hybrid technique combines the sensitivity-based approach with an energy-based technique to increase the efficiency of gate delay propagation. Experimental results demonstrate the higher accuracy of our methodology compared to the best of the existing techniques. The sensitivity-based technique is compatible with the current level of gate characterization in conventional ASIC cell libraries, and so it can be easily incorporated into commercial STA tools to enhance their accuracy.

Design of a Universal Logic Block for Fault-Tolerant Realization of any Logic Operation in Trapped-Ion Quantum Circuits

Abstract: This paper presents a physical mapping tool for quantum circuits, which generates the optimal Universal Logic Block (ULB) that can perform any logical fault-tolerant (FT) quantum operations with the minimum latency. The operation scheduling, placement, and qubit routing problems tackled by the quantum physical mapper are highly dependent on one another. More precisely, the scheduling solution affects the quality of the achievable placement solution due to resource pressures that may be created as a result of operation scheduling whereas the operation placement and qubit routing solutions influence the scheduling solution due to resulting distances between predecessor and current operations, which in turn determines routing latencies. The proposed flow for the quantum physical mapper captures these dependencies by applying (i) a loose scheduling step, which transforms an initial quantum data flow graph into one that explicitly captures the no-cloning theorem of the quantum computing and then performs instruction scheduling based on a modified force-directed scheduling approach to minimize the resource contention and quantum circuit latency, (ii) a placement step, which uses timing-driven instruction placement to minimize the approximate routing latencies while making iterative calls to the aforesaid force-directed scheduler to correct scheduling levels of quantum operations as needed, and (iii) a routing step that finds dynamic values of routing latencies for the qubits. In addition to the quantum physical mapper, an approach is presented to determine the single best ULB size for a target quantum circuit by examining the latency of different FT quantum operations mapped onto different ULB sizes and using information about the occurrence frequency of operations on critical paths of the target quantum algorithm to weigh these latencies.

Optimal design and management of a smart residential PV and energy storage system

Abstract: Solar photovoltaic (PV) technology has been widely deployed in large power plants operated by utility companies. However, the home owners are not yet convinced of the saving cost benefits of this technology, and consequently, in spite of government subsidies, they have been reluctant to install PV systems in their homes. The main reason for this is the absence of a complete and truthful analysis which could explain to home owners under what conditions spending money on a PV system can actually save them money over a long-term, but known, time horizon. This paper thus presents a design and management mechanism for a smart residential energy system comprising PV modules, electrical energy storage banks, and conversion circuits connected to the power grid. First, we figure out how much savings can be achieved by a system with given PV modules and EES bank capacities by optimally solving the daily energy flow control problem of such a system. Based on the daily optimization results, we come up with the optimal system specifications with a fixed budget. Experiments are conducted for various electricity prices and different profiles of PV output power and load demand. Results show that the designed system breaks even in 6 years and in the system lifetime achieves up to 8% annual profit besides paying back the budget.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.