Random and Adversarial Bit Error Robustness: {E}nergy-Efficient and Secure {DNN} Accelerators

Deep neural network (DNN) accelerators received considerable attention in recent years due to the potential to save energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. Furthermore, DNN accelerators have been shown to be vulnerable to adversarial attacks on voltage controllers or individual bits. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, as well as random bit error training (RandBET) or adversarial bit error training (AdvBET) improves robustness against random or adversarial bit errors in quantized DNN weights significantly. This leads not only to high energy savings for low-voltage operation as well as low-precision quantization, but also improves security of DNN accelerators. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays, and achieves robustness against both targeted and untargeted bit-level attacks. Without losing more than 0.8%/2% in test accuracy, we can reduce energy consumption on CIFAR10 by 20%/30% for 8/4-bit quantization using RandBET. Allowing up to 320 adversarial bit errors, AdvBET reduces test error from above 90% (chance level) to 26.22% on CIFAR10

Bit Error Robustness for Energy-Efficient {DNN} Accelerators

Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, and random bit error training (RandBET) improves robustness against random bit errors in (quantized) DNN weights significantly. This leads to high energy savings from both low-voltage operation as well as low-precision quantization. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays. We also discuss why weight clipping alone is already a quite effective way to achieve robustness against bit errors. Moreover, we specifically discuss the involved trade-offs regarding accuracy, robustness and precision: Without losing more than 1% in accuracy compared to a normally trained 8-bit DNN, we can reduce energy consumption on CIFAR-10 by 20%. Higher energy savings of, e.g., 30%, are possible at the cost of 2.5% accuracy, even for 4-bit DNNs

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Variational optimization and data assimilation in chaotic time-delayed systems with automatic-differentiated shadowing sensitivity

In this computational paper, we perform sensitivity analysis of long-time (or ensemble) averages in the chaotic regime using the shadowing algorithm. We introduce automatic differentiation to eliminate the tangent/adjoint equation solvers used in the shadowing algorithm. In a gradient-based optimization, we use the computed shadowing sensitivity to minimize different long-time averaged functionals of a chaotic time-delayed system by optimal parameter selection. In combined state and parameter estimation for data assimilation, we use the computed sensitivity to predict the optimal trajectory given information from a model and data from measurements beyond the predictability time. The algorithms are applied to a thermoacoustic model. Because the computational framework is rather general, the techniques presented in this paper may be used for sensitivity analysis of ensemble averages, parameter optimization and data assimilation of other chaotic problems, where shadowing methods are applicable

Clinical Relevance and Antimicrobial Profiling of Methicillin-Resistant Staphylococcus aureus (MRSA) on Routine Antibiotics and Ethanol Extract of Mango Kernel (Mangifera indica L.)

Methicillin-resistant Staphylococcus aureus (MRSA) is known for serious health problems. Testing new inexpensive natural products such as mango kernel (Mangifera indica L., Anacardiaceae) may provide alternative and economically viable anti-MRSA drugs. In the current study, we screened clinical isolates from Aseer Central Hospital, Saudi Arabia, during 2012–2017 for MRSA and tested an ethanolic extract of mango kernel for anti-MRSA activity. Brief confirmation of MRSA was performed by the Vitek 2 system, while antibiotic sensitivity of strains was tested for their clinical relevance. The In vitro disc diffusion method was used to test the anti-MRSA activity of the ethanolic mango kernel extract. The antimicrobial activity of mango kernel was compared to that of standard drugs (oxacillin and vancomycin). Of the identified 132 S. aureus strains, 42 (31.8%) were found to be MRSA and their prevalence showed a clear increase during the last two years (2016-2017; p<0.001). MRSA strains showed 100% sensitivity to vancomycin, teicoplanin, linezolid, tetracycline, daptomycin, tigecycline, and tobramycin and 100% resistance to ampicillin and 98% to penicillin. The ethanolic extracts of mango kernel were found active against both S. aureus and the MRSA strains. Inhibitory activities (mean ± SE) were achieved at concentrations of 50 mg/mL (20.77 ± 0.61), 5 mg/mL (16.18 ± 0.34), and 0.5 mg/mL (8.39 ± 0.33) exceeding that of vancomycin (p=0.0162). MRSA strains were sensitive to mango kernel extracts when compared to vancomycin. Therefore, ethanolic extracts of mango kernel can be escalated to animal model studies as a promising leading anti-MRSA drug candidate and can be an economic alternative to high-priced synthetic antibiotics