Publications

This paper concerns a method for digital circuit optimization based on formulating the problem as a geometric program (GP) or generalized geometric program (GGP), which can be transformed to a convex optimization problem and then very efficiently solved. We start with a basic gate scaling problem, with delay modeled as a simple resistor-capacitor (RC) time constant, and then add various layers of complexity and modeling accuracy, such as accounting for differing signal fall and rise times, and the effects of signal transition times. We then consider more complex formulations such as robust design over corners, multimode design, statistical design, and problems in which threshold and power supply voltage are also variables to be chosen. Finally, we look at the detailed design of gates and interconnect wires, again using a formulation that is compatible with GP or GGP.

Recounts the career and contributions of Paul Penfield Jr.

Several risk prediction models for cardiovascular disease are available and their head to head comparisons would benefit from standardised reporting and formal, consistent statistical comparisons. Outcome selection and optimism biases apparently affect this literature.

Synthetic aperture focusing consists of warping and adding together the images in a 4D light field so that objects lying on a specified surface are aligned and thus in focus, while objects lying off this surface are misaligned and hence blurred. This provides the...

Abstract Background It has been proposed that the threshold of statistical significance should shift from P-value < 0.05 to P-value < 0.005, but there is concern that this move may dismiss effective, useful interventions. We aimed to assess how often medical interventions are recommended although their evidence in meta-analyses of randomized trials lies between P-value = 0.05 and P-value = 0.005. Methods We included Cochrane systematic reviews (SRs) published from 1 January 2013 to 30 June 2014 that had at least one meta-analysis with GRADE (Grading of Recommendations Assessment, Development and Evaluation) assessment and at least one primary outcome having favourable results for efficacy at P-value < 0.05. Only comparisons of randomized trials between active versus no treatment/placebo were included. We then assessed the respective UpToDate recommendations for clinical practice from 22 May 2018 to 5 October 2018 and recorded how many treatments were recommended and what were the P-values in their meta-analysis evidence. The primary analysis was based on the first-listed outcomes. Results Of 608 screened SRs with GRADE assessment, 113 SRs were eligible, including 143 comparisons of which 128 comparisons had first-listed primary outcomes with UpToDate coverage. Altogether, 60% (58/97) of interventions with P-values < 0.005 for their evidence were recommended versus 32% (10/31) of those with P-value 0.005–0.05. Therefore, most (58/68, 85.2%) of the recommended interventions had P-values < 0.005 for the first-listed primary outcome. Of the 10 exceptions, 4 had other primary outcomes with P-values < 0.005 and another 4 had additional extensive evidence for similar indications that would allow extrapolation for practice recommendations. Conclusions Few interventions are recommended without their evidence from meta-analyses of randomized trials reaching P-value < 0.005.

A backplane transceiver uses a folded 5-tap TX equalizer and 5-tap RX equalizer to counteract losses and reflections. A flexible 2-PAM/4-PAM CDR uses select transitions for receive clock recovery. BER better than 10/sup -15/ at 10 Gb/s and power <60 mW/Gb are measured values for a 16" backplane with two high speed connectors.

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120x energy saving, Exploiting sparsity saves 10x, Weight sharing gives 8x, Skipping zero activations from ReLU saves another 3x. Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102 GOPS working directly on a compressed network, corresponding to 3 TOPS on an uncompressed network, and processes FC layers of AlexNet at 1.88x104 frames/sec with a power dissipation of only 600mW. It is 24,000x and 3,400x more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy efficiency and area efficiency.

<title>Abstract</title> Background: Among different investigators studying the same exposures and outcomes, there may be a lack of consensus about potential confounders that should be considered as matching, adjustment, or stratification variables in observational studies. Concerns have been raised that confounding factors may affect the results obtained for the alcohol-ischemic heart disease relationship, as well as their consistency and reproducibility across different studies. Therefore, we assessed how confounders are defined, operationalized, and discussed across individual studies evaluating alcohol and ischemic heart disease risk. Methods: For observational studies included in a recent alcohol-ischemic heart disease meta-analysis, we identified all variables adjusted, matched, or stratified for in the largest reported multivariate model (i.e. potential confounders). We recorded how the variables were measured and grouped them into higher-level confounder domains. Abstracts and Discussion sections were then assessed to determine whether authors considered confounding when interpreting their study findings. Results: 85 of 87 (97.7%) studies reported multivariate analyses for an alcohol-ischemic heart disease relationship. The most common higher-level confounder domains included were smoking (79, 92.9%), age (74, 87.1%), and BMI, height, and/or weight (57, 67.1%). However, no two models adjusted, matched, or stratified for the same higher-level confounder domains. Most (74/87, 85.1%) articles mentioned or alluded to “confounding” in their Abstract or Discussion sections, but only one stated that their main findings were likely to be affected by residual confounding. There were five (5/87, 5.7%) authors that explicitly asked for caution when interpreting results. Conclusion: There is large variation in the confounders considered across observational studies evaluating alcohol and ischemic heart disease risk and almost all studies spuriously ignore or eventually dismiss confounding in their conclusions. Given that study results and interpretations may be affected by the mix of potential confounders included within multivariate models, efforts are necessary to standardize approaches for selecting and accounting for confounders in observational studies.