Can we trust undervolting in FPGA-based deep learning designs at harsh conditions?

As more Neural Networks on Field Programmable Gate Arrays (FPGAs) are used in a wider context, the importance of power efficiency increases. However, the focus on power should never compromise application accuracy. One technique to increase power efficiency is reducing the FPGAs' supply voltage ("undervolting"), which can cause accuracy problems. Therefore, careful design-time considerations are required for correct configuration without hindering the target accuracy. This fact becomes especially important for autonomous systems, edge-computing, or data-centers. This study reveals the impact of undervolting in harsh environmental conditions on the accuracy and power efficiency of the convolutional neural network benchmarks. We perform the comprehensive testing in a calibrated infrastructure at controlled temperatures (between -40C and 50C) and four distinct humidity levels (40%, 50%, 70%, 80%) for off-the-shelf FPGAs. We show the voltage guard-band shift with temperature is linear and propose new reliable undervolting designs providing a 65% increase in power efficiency (GOPS/W).Peer ReviewedPostprint (author's final draft

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc

An experimental study of reduced-voltage operation in modern FPGAs for neural network acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect ofenvironmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W ) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.The work done for this paper was partially supported by a HiPEAC Collaboration Grant funded by the H2020 HiPEAC Project under grant agreement No. 779656. The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the LEGaTO Project (www.legato-project.eu), grant agreement No. 780681.Peer ReviewedPostprint (author's final draft

Ectopic Reticulum in a Cow

A two years-old Holstein cow with poor appetite, reduced milk production, and partial defecation was evaluated in the present case report. After routine laboratory and clinical examinations, the animal further received ultrasound examination and then a right fossa paralumbal exploratory laparotomy was performed to the cow. The cow was diagnosed with ectopic reticulum on the laparotomy. After the content of the reticulum was removed, liquid paraffin was administered into the reticulum and its wall and abdominal wall was sutured as routinely. The prognosis of the animal deteriorated gradually following to the laparotomy and it was slaughtered by its owner. This is the first report showing the presence of an ectopic reticulum in a cow. (C) 2016 PVJ. All rights reserve

Impact of bacterial translocation in calves with atresia coli

Objective - To identify whether enteric bacteria pass into the mesenteric lymph nodes (MLNs) and peritoneal cavity in calves with atresia coli and to evaluate whether the presence of bacterial translocation (BT) has an impact on the success of surgical treatment. Design - Prospective clinical study. Animals - Twenty-six client-owned calves. Interventions - During laparotomy, swab samples were collected from the peritoneal cavity and MLNs using a sterile swab stick and were submitted for microbiological analysis. Measurements and Main Results - Bacterial cultures of swab samples revealed that 65% (n = 17) of the calves experienced BT. Of these, 14 calves experienced BT to the MLNs, 9 to the peritoneal cavity, and 5 to both regions. Of the bacteria isolated from the MLNs, 72% (n = 10) were Escherichia coli. Of the samples isolated from the peritoneal fluid, 33% (n = 3) contained E. coli and 33% (n = 3) contained E. coli + coagulase-negative Staphylococcus (CNS). In calves with BT that were discharged (n = 13) and without BT that were discharged (n = 7), the median survival was 30 days; these data were found to be similar in the 2 groups. Conclusions - This study revealed that BT is observed in the majority of atresia coli cases. E. coli is more common in BT, and translocation occurs primarily through the lymphatic route. These results suggest that the presence of BT is closely related to the success of the operation for correction of atresia coli

Effects of annealing temperature on a ZnO thin film-based ultraviolet photodetector

In this paper, the effects of annealing temperature on the performance of a ZnO thin film-based Metal-Semiconductor-Metal (MSM) type ultraviolet (UV) photodetector is reported. ZnO thin films were grown on a glass substrate using the Pulsed Filtered Cathodic Vacuum Arc Deposition (PFCVAD) technique at room temperature and after the deposition process the samples were annealed at 400, 450 and 550 degrees C in air condition to investigate the annealing effect on the structural, electrical, and optical properties of the photodetector. ZnO thin films which have grains in nanometer range has an increasing in the diameter of grains from 10.5 to 18.3 nm as a function of annealing temperature results in a red shift in the cut-off wavelength of the photodetector from 3.25 eV (381 nm) to 3.23 eV (383 nm). It is demonstrated that the sensitivity and the speed (rise/fall times) of the ZnO thin film based MSM photodetectors enhances with increasing post growth annealing temperature of ZnO thin film due to the increase in the absorption coefficient and the decrease of the total area of the grain boundaries due to the larger grain sizes formation in ZnO thin films with increasing thermal annealing temperature

Investigation of the skin characteristics in patients with severe GH deficiency and the effects of 6 months of GH replacement therapy: a randomized placebo controlled study

Objective The presence of GH receptor in human skin and its appendages suggests a direct effect of GH on skin characteristics. The skin is usually thin and dry in patients with GH deficiency (GHD). Sheehan's syndrome classically refers to postpartum hypopituitarism and GH is one of the earliest pituitary hormones lost. While severe GHD is a well-established feature of Sheehan's Syndrome, skin characteristics and the effects of GH replacement therapy (GHRT) have been investigated neither in Sheehan's syndrome nor in other disorders of GHD. The aim of this study was to investigate the skin characteristics, including the sebum content, hydration (skin capacitance), transepidermal water loss (TEWL), pH and skin temperature, and particularly the effects of 6 months of GHRT on these parameters in GH deficient patients with Sheehan's syndrome