Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus, they are useful to enable energy-efficient hardware inference. However, exploiting spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. In this work, we propose an FPGA-based convolutional SNN accelerator called Skydiver that exploits spatio-temporal workload balance. We propose the Approximate Proportional Relation Construction (APRC) method that can predict the relative workload channel-wisely and a Channel-Balanced Workload Schedule (CBWS) method to increase the hardware workload balance ratio to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on image segmentation and MNIST classification tasks. Results show improved throughput by 1.4X and 1.2X for the two tasks. Skydiver achieved 22.6 KFPS throughput, and 42.4 uJ/Image prediction energy on the classification task with 98.5% accuracy.Comment: Accepted to be published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 202

FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation

Fast video recognition is essential for real-time scenarios, e.g., autonomous driving. However, applying existing Deep Neural Networks (DNNs) to individual high-resolution images is expensive due to large model sizes. Spiking Neural Networks (SNNs) are developed as a promising alternative to DNNs due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus they are useful to enable energy-efficient computation. However, exploiting the spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading energy efficiency. In this work, we, therefore, propose an SNN accelerator called FrameFire for efficient video processing. We introduce a Keyframe-dominated Workload Balance Schedule (KWBS) method. It accelerates the image recognition network with sparse keyframes, then records and analyzes the current workload distribution on hardware to facilitate scheduling workloads in subsequent frames. FrameFire is implemented on a Xilinx XC7Z035 FPGA and verified by video segmentation tasks. The results show that the throughput is improved by 1.7× with the KWBS method. FrameFire achieved 1.04 KFPS throughput and 1.15 mJ/frame recognition energy

The role of crosslinking density in surface stress and surface energy of soft solids

Surface stress and surface energy are two fundamental parameters that determine the surface properties of any materials. While it is commonly believed that the surface stress and surface energy of liquids are identical, the relationship between the two parameters in soft polymeric gels remains debatable. In this work, we measured the surface stress and surface energy of soft silicone gels with varying weight ratios of crosslinkers in soft wetting experiments. Above a critical density, $k_0$, the surface stress was found to increase significantly with crosslinking density while the surface energy remained unchanged. In this regime, we can estimate a non-zero surface elastic modulus that also increases with the ratio of crosslinkers. By comparing the surface mechanics of the soft gels with their bulk rheology, the surface properties near the critical density $k_0$ were found to be closely related to the underlying percolation transition of the polymer networks.Comment: 9 pages, 7 figure

Laser-assisted grinding of reaction-bonded SiC

The paper presents development of a novel laser-assisted grinding process to reduce surface roughness and subsurface damage in grinding reaction-bonded (RB)-SiC. A thermal control approach is proposed to facilitate the process development, in which a two-temperature model is applied to control the required laser power to thermal softening of RB-SiC prior to grinding operation without melting the workpiece or leaving undesirable microstructural alteration, while Fourier's law is adopted to obtain the thermal gradient for verification. An experimental comparison of conventional grinding and laser-assisted grinding shows significant reduction of machined surface roughness (37%-40%) and depth of subsurface damage (SSD) layer (22%-50.6%) using the thermal control approach under the same grinding conditions. It also shows high specific grinding energy 1.5 times that in conventional grinding at the same depth of cut which accounts for the reduction of subsurface damage as it provides enough energy to promote ductile-regime material removal

Elasticity-Controlled Jamming Criticality in Soft Composite Solids

Soft composite solids are made of dispersed inclusions within soft matrices. They are ubiquitous in nature and form the basis of many biological tissues. In the field of materials science, synthetic soft composites are promising candidates for constructing various engineering devices due to their highly programmable features. However, when the volume fraction of inclusions increases, predicting the mechanical properties of these materials poses a significant challenge for the classical theories in composite mechanics. The difficulty arises from the inherently disordered, multi-scale interactions between the inclusions and matrix. To address this challenge, we conducted systematic investigations on the mechanics of densely-filled soft elastomers containing stiff microspheres. We experimentally demonstrated how the strain-stiffening response of the soft composites is governed by the critical scalings in the vicinity of a continuous phase transition, which depend on both the elasticity of the elastomer matrix and the particles. The critical points signify a shear-jamming transition of the included particles in the absence of matrix elasticity. The proposed criticality framework quantitatively predicts diverse mechanical responses observed in experiments across a wide range of material parameters. The findings uncover a novel design paradigm of composite mechanics that relies on engineering the jamming-criticality of the embedded inclusions

Robust output-feedback predictive control for proximity eddy current de-tumbling with constraints and uncertainty

Proximity operation can significantly improve the efficiency of eddy current de-tumbling. However, the tumbling motion and non-cooperation of space debris make the chaser execute collision avoidance maneuvers and be influenced by model uncertainty. In this paper, an inertial-oriented safety corridor is proposed by taking the debris' angular momentum as the central axis, which can avoid the frequent collision maneuvers of the chaser. Meanwhile, a desired de-tumbling trajectory under this safety corridor is designed to de-tumble the angular velocity of space debris. Then, a robust output-feedback controller considering safety corridor and model uncertainty is proposed by combining moving horizon estimation and model predictive control. The moving horizon estimation is employed to estimate the system state and model uncertainty which is compensated by a feedforward control law. Furthermore, the model predictive control without terminal ingredients is designed to realize the optimal performance of fuel consumption and the robust tracking stability of the system. Finally, taking the Chinese Sinosat-2 satellite as the simulation case, the effectiveness of the proposed scheme is verified

Double-zero-event studies matter: A re-evaluation of physical distancing, face masks, and eye protection for preventing person-to-person transmission of COVID-19 and its policy impact

In a recent timely systematic review, Chu et al. [1] assessed the effectiveness of face masks, eye protection, and physical distancing for preventing COVID-19. Because the sample sizes are not large, especially in some studies of COVID-19, this review contains a considerable number of studies with zero counts of infection events, creating challenges in estimating effect sizes. If zero counts appear in both groups, this double-zero-event study (DZS) is omitted from the analyses, as implied in the forest plots in Chu et al. [1] Specifically, at least 9 out of 44 studies in this review are DZS with 1784 subjects. An omission of information about the rare outcome in DZS or artificial correction of the zero counts could impact the conclusions. [2], [3], [4], [5].This research was supported in part by the U.S. National Institutes of Health/National Library of Medicine grant R01 LM012982. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis

Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has been widely employed as an analytical tool in several industries. However, most chemical processes or analytes are multivariate and nonlinear in nature. To solve this problem, local errors regression method is presented in order to build an accurate calibration model in this paper, where a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors. After the selection of calibration subset, the partial least squares regression is applied to build calibration model. The performance of the proposed method is demonstrated through a near-infrared spectroscopy dataset of pharmaceutical tablets. Compared with other local strategies with different similarity criterions, it has been shown that the proposed local errors regression can result in a significant improvement in terms of both prediction ability and calculation speed

Large scale fabrication of nitrogen vacancy-embedded diamond nanostructures for single-photon source applications

Some color centers in diamond can serve as quantum bits which can be manipulated with microwave pulses and read out with laser, even at room temperature. However, the photon collection efficiency of bulk diamond is greatly reduced by refraction at the diamond/air interface. To address this issue, we fabricated arrays of diamond nanostructures, differing in both diameter and top end shape, with HSQ and Cr as the etching mask materials, aiming toward large scale fabrication of single-photon sources with enhanced collection efficiency made of nitrogen vacancy (NV) embedded diamond. With a mixture of O2 and CHF3 gas plasma, diamond pillars with diameters down to 45 nm were obtained. The top end shape evolution has been represented with a simple model. The tests of size dependent single-photon properties confirmed an improved single-photon collection efficiency enhancement, larger than tenfold, and a mild decrease of decoherence time with decreasing pillar diameter was observed as expected. These results provide useful information for future applications of nanostructured diamond as a single-photon source