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.

Design and analysis of segmented routing channels for row-based FPGA's

Abstract: FPGA's combine the logic integration benefits of custom VLSI with the design, production, and time-to-market advantages of standard logic IC's. The Actel family of FPGA's exemplifies the row-based FPGA model. Rows of logic cells interspersed with routing channels have given this family of FPGA devices the flavor of traditional channeled gate arrays or standard cells. However, unlike the conventional standard cell design, the FPGA routing channels contain predefined wiring segments of various lengths that are interconnected using antifuses. This paper develops analytical models that permit the design of FPGA channel architecture and the analysis of the routability of row-based FPGA devices based on a generic characterization of the row-based FPGA routing algorithms. In particular, it demonstrates that using probabilistic models for the origination point and length of connections, an FPGA with properly designed segment length and distribution can be nearly as efficient as a mask-programmable channel (in terms of the number of required tracks). Experimental results corroborate this prediction. This paper does not address specifics of the routing algorithms, but investigates the design of the channel segmentation architecture (i.e., various lengths and patterns of segments and connections among these segments) in order to increase the probability of successful routing. >

Logic Optimization, Complex Cell Design, and Retiming of Single Flux Quantum Circuits

Abstract: Single flux quantum (SFQ) technology has emerged as a promising beyond-CMOS technology with Josephson junctions (JJs) as active devices in a superconducting circuit. In this paper, we (i) describe the algorithms and the methodology we used to synthesize a synchronous SFQ circuit using a CMOS logic synthesis tool by adding new features to the tool and by modifying the existing features; (ii) introduce the concept of two kinds of complex cells: (a) stand-alone cells and (b) interconnected cells along with the advantage gained by these complex cells through the results of synthesized SFQ netlists. Design of stand-alone complex cells presented here includes and and or gates with more-than-two inputs, high-fanout splitters, and “A+BC” cell. Circuits of decoders, multiplexers, and carrylook-ahead adders are synthesized with complex cells, and the advantage in terms of JJ count and latency is presented. We have also investigated the possibility of SFQ cells supporting multiple fanout drive capability by modifying the input/output interfaces of SFQ standard cells. In this direction, an algorithm to modify the input interface of a cell so that it can be driven along with similar cells by a single splitter output is presented along with the results for a simple clock-distribution line with multiple-fanout capability.

Model-Free Reinforcement Learning and Bayesian Classification in System-Level Power Management

Abstract: To cope with uncertainties and variations that emanate from hardware and/or application characteristics, dynamic power management (DPM) frameworks must be able to learn about the system inputs and environmental variations, and adjust the power management policy on the fly. In this paper, an online adaptive DPM technique is presented based on the model-free reinforcement learning (RL) method, which requires no prior knowledge of the state transition probability function and the reward function. In particular, this paper employs the temporal difference (TD) learning method for semi-Markov decision process (SMDP) as the model-free RL technique since the TD method can accelerate convergence and alleviate the reliance on the Markovian property of the power-managed system. In addition, a novel workload predictor based on an online Bayesian classifier is presented to provide effective estimation of the workload characteristics for the RL algorithm. Several improvements are proposed to manage the size of the action space for the learning algorithm, enhance its convergence speed, and dynamically change the action set associated with each system state. In the proposed DPM framework, power-latency tradeoffs of the power-managed system can be precisely controlled based on a user-defined parameter. Extensive experiments on hard disk drives and wireless network cards show that the maximum power saving without sacrificing any latency is 18.6 percent compared to a reference expert-based approach. Alternatively, the maximum latency saving without any power dissipation increase is 73.0 percent compared to the existing best-of-breed DPM techniques.

Joint sizing and adaptive independent gate control for FinFET circuits operating in multiple voltage regimes using the logical effort method

Abstract: FinFET has been proposed as an alternative for bulk CMOS in current and future technology nodes due to more effective channel control, reduced random dopant fluctuation, high ON/OFF current ratio, lower energy consumption, etc. Key characteristics of FinFET operating in the sub/near-threshold region are very different from those in the strong-inversion region. This paper first introduces an analytical transregional FinFET model with high accuracy in both sub- and near-threshold regimes. Next, the paper extends the well-known and widely-adopted logical effort delay calculation and optimization method to FinFET circuits operating in multiple voltage (sub/near/super-threshold) regimes. More specifically, a joint optimization of gate sizing and adaptive independent gate control is presented and solved in order to minimize the delay of FinFET circuits operating in multiple voltage regimes. Experimental results on a 32nm Predictive Technology Model for FinFET demonstrate the effectiveness of the proposed logical effort-based delay optimization framework.

Adaptive models for input data compaction for power simulators

Abstract: Presents an effective and robust technique for compacting a large sequence of input vectors into a much smaller input sequence so as to reduce the circuit/gate-level simulation time by orders of magnitude and maintain the accuracy of the power estimates. In particular, this paper introduces and characterizes a family of dynamic Markov trees that can model complex the spatiotemporal correlations which occur during power estimation in both combinational and sequential circuits. As the results demonstrate, large compaction ratios of 1-2 orders of magnitude can be obtained without a significant loss (less than 5% on average) in the accuracy of the power estimates.

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.