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.

A Logic Verification Framework for SFQ and AQFP Superconducting Circuits

Abstract: Traditional logical equivalence checking (LEC), which plays a major role in the entire chip design process, faces challenges of meeting the requirements demanded by the many emerging technologies that are based on logic models different from the standard complementary metal oxide semiconductor (CMOS). In this article, we propose an LEC framework to be employed in the verification process of superconducting electronics (SCE). Our LEC framework is compatible with the existing CMOS technologies and can also check features and capabilities that are unique to SCE. For instance, the performance of nonresistively biased single-flux quantum (SFQ) circuits benefits from ultradeep pipelining, and verification of such circuits requires new models and algorithms. We, therefore, present the multicycle input dependency circuit model which is a novel model representation of design to explicitly capture the dependency of primary outputs of the circuit on sequences of internal signals and inputs. Embedding the proposed circuit model and several structural checking modules, the process of verification can be independent of the underlying technology and signaling. We benchmark the proposed framework on postsynthesis SFQ and 4-phase adiabatic quantum flux parametron netlists. Results show a comparative verification time of SFQ circuit benchmark, including 16-bit integer divider and ISCAS’85 circuits with respect to the ABC tool for similar CMOS circuits.

A thermally-aware energy minimization methodology for global interconnects

Abstract: As a result of the Temperature Effect Inversion (TEI) in FinFET-based designs, gate delays decrease with the increase of temperature. In contrast, the resistive characteristic and hence delay of global interconnects increase with the temperature. However, as shown in this paper, if buffers are judiciously inserted in global interconnects, the buffer delay decrease is more pronounced than the interconnect delay increase, resulting in an overall performance improvement at higher temperatures. More specifically, this work models the delay of buffer-inserted global interconnects vs. temperature in order to derive the optimal number and size of buffers for a given interconnect length and temperature. Furthermore, the paper addresses the problem of minimizing the buffered interconnect energy consumption by changing the supply voltage level or FinFET threshold voltage, and also presents a temperature-aware optimization policy for solving this problem. Simulation results show average interconnect energy savings of 16% with no performance penalty for five different benchmarks implemented on a 14nm FinFET technology.

Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression.

Abstract: Deep spiking neural networks (SNNs) have emerged as a potential alternative to traditional deep learning frameworks, due to their promise to provide increased compute efficiency on event-driven neuromorphic hardware. However, to perform well on complex vision applications, most SNN training frameworks yield large inference latency which translates to increased spike activity and reduced energy efficiency. Hence,minimizing average spike activity while preserving accuracy indeep SNNs remains a significant challenge and opportunity.This paper presents a non-iterative SNN training technique thatachieves ultra-high compression with reduced spiking activitywhile maintaining high inference accuracy. In particular, our framework first uses the attention-maps of an un compressed meta-model to yield compressed ANNs. This step can be tuned to support both irregular and structured channel pruning to leverage computational benefits over a broad range of platforms. The framework then performs sparse-learning-based supervised SNN training using direct inputs. During the training, it jointly optimizes the SNN weight, threshold, and leak parameters to drastically minimize the number of time steps required while retaining compression. To evaluate the merits of our approach, we performed experiments with variants of VGG and ResNet, on both CIFAR-10 and CIFAR-100, and VGG16 on Tiny-ImageNet.The SNN models generated through the proposed technique yield SOTA compression ratios of up to 33.4x with no significant drops in accuracy compared to baseline unpruned counterparts. Compared to existing SNN pruning methods, we achieve up to 8.3x higher compression with improved accuracy.

LCHC-DFT: A Low-Cost High-Coverage Design-for-Testability Technique to Detect Hard-to-Detect Faults in STT-MRAMs in the Presence of Process Variations

Abstract: This paper proposes a low-cost yet high-coverage design-for-testability (DFT) scheme for improving the detection of hard-to-detect (HtD) faults in STT-MRAMs. It is based on introducing a voltage mismatch in the sense amplifier (SA). This mismatch along with the defects in the cell may bias the SA, leading to an incorrect read output which in turn make the HtD faults detectable. The efficacy of the proposed scheme is evaluated by calculating its detection capability in the presence of resistive defects. Evaluation results obtained for a 20nm STT-MRAM memory design show that the proposed scheme provides HtD fault coverage of 81.8% and 86.5%, on average, for intra-cell and inter-cell defects, respectively, with a negligible area overhead (1.1% for a 2Kbit array). In comparison with conventional March tests, which only detect easy-to-detect (EtD) faults, the proposed DFT technique covers both HtD and EtD faults with a faster approach, namely by using a single read operation. Furthermore, to guarantee that the cells affected by HtD faults trigger an incorrect read output under process variation (PV), a timing strategy based on modifying the control unit of the proposed DFT is suggested. The strategy controls the stress introduced by the DFT unit by altering the timing of DFT control signals. The timing alteration enables the detection of HtD faults in the devices with higher deviations from the nominal one due to PV, avoiding yield loss or test escapes.

OPLE: A Heuristic Custom Instruction Selection Algorithm Based on Partitioning and Local Exploration of Application Dataflow Graphs

Abstract: In this article, a heuristic custom instruction (CI) selection algorithm is presented. The proposed algorithm, which is called OPLE for “Optimization based on Partitioning and Local Exploration,” uses a combination of greedy and optimal optimization methods. It searches for the near-optimal solution by reducing the search space based on partitioning the identified CI set. The partitioning of the identified set guarantees the success of the algorithm independent of the size of the identified set. First, the algorithm finds the near-optimal CIs from the candidate CIs for each part. Next, the suggested CIs from different parts are combined to determine the final selected CI set. To improve the set of the selected CIs, the solution is evolved by calling the algorithm iteratively. The efficacy of the algorithm is assessed by comparing its performance to those of optimal and nonoptimal methods. A comparative study is performed for a number of benchmarks under different area budgets and I/O constraints. The results reveal higher speedups for the OPLE algorithm, especially for larger identified candidate sets and/or small area budgets compared to those of the nonoptimal solutions. Compared to the nonoptimal techniques, the proposed algorithm provides 30p higher speedup improvement on average. The maximum improvement is 117p. The results also demonstrate that in many cases OPLE is able to find the optimal solution.

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.