RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network

Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the event data requires either using expensive feature descriptors to transform spikes into frames, or using spiking neural networks that are expensive to train. In this work, we propose a neural network architecture, Reservoir Nodes-enabled neuromorphic vision sensing Network (RN-Net), based on simple convolution layers integrated with dynamic temporal encoding reservoirs for local and global spatiotemporal feature detection with low hardware and training costs. The RN-Net allows efficient processing of asynchronous temporal features, and achieves the highest accuracy of 99.2% for DVS128 Gesture reported to date, and one of the highest accuracy of 67.5% for DVS Lip dataset at a much smaller network size. By leveraging the internal device and circuit dynamics, asynchronous temporal feature encoding can be implemented at very low hardware cost without preprocessing and dedicated memory and arithmetic units. The use of simple DNN blocks and standard backpropagation-based training rules further reduces implementation costs.Comment: 12 pages, 5 figures, 4 table

PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators

Analog compute-in-memory (CIM) accelerators are becoming increasingly popular for deep neural network (DNN) inference due to their energy efficiency and in-situ vector-matrix multiplication (VMM) capabilities. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. In this paper, we identify a security vulnerability wherein an adversary can reconstruct the user's private input data from a power side-channel attack, under proper data acquisition and pre-processing, even without knowledge of the DNN model. We further demonstrate a machine learning-based attack approach using a generative adversarial network (GAN) to enhance the reconstruction. Our results show that the attack methodology is effective in reconstructing user inputs from analog CIM accelerator power leakage, even when at large noise levels and countermeasures are applied. Specifically, we demonstrate the efficacy of our approach on the U-Net for brain tumor detection in magnetic resonance imaging (MRI) medical images, with a noise-level of 20% standard deviation of the maximum power signal value. Our study highlights a significant security vulnerability in analog CIM accelerators and proposes an effective attack methodology using a GAN to breach user privacy

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue‐memory‐based neuromorphic computing can be orders of magnitude more energy efficient at data‐intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer‐sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria‐stabilized zirconia (YSZ), toward eliminating filaments. Filament‐free, bulk‐RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk‐RRAM devices using TiO2‐X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk‐RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy‐efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.A resistive memory cell based on the electrochemical migration of oxygen vacancies for in‐memory neuromorphic computing is presented. By using the average statistical behavior of all oxygen vacancies to store analogue information states, this cell overcomes the stochastic and unpredictable switching plaguing filament‐forming memristors, and instead achieves linear, predictable, and deterministic switching.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163547/3/adma202003984_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163547/2/adma202003984-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163547/1/adma202003984.pd

SALM5 trans-synaptically interacts with LAR-RPTPs in a splicing-dependent manner to regulate synapse development

Synaptogenic adhesion molecules play critical roles in synapse formation. SALM5/Lrfn5, a SALM/Lrfn family adhesion molecule implicated in autism spectrum disorders (ASDs) and schizophrenia, induces presynaptic differentiation in contacting axons, but its presynaptic ligand remains unknown. We found that SALM5 interacts with the Ig domains of LAR family receptor protein tyrosine phosphatases (LAR-RPTPs; LAR, PTPδ, and PTPσ). These interactions are strongly inhibited by the splice insert B in the Ig domain region of LAR-RPTPs, and mediate SALM5-dependent presynaptic differentiation in contacting axons. In addition, SALM5 regulates AMPA receptor-mediated synaptic transmission through mechanisms involving the interaction of postsynaptic SALM5 with presynaptic LAR-RPTPs. These results suggest that postsynaptic SALM5 promotes synapse development by trans-synaptically interacting with presynaptic LAR-RPTPs and is important for the regulation of excitatory synaptic strength

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Biologically plausible computing systems require fine- grain tuning of analog synaptic characteristics. In this study, lithium- doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state- dependent decay to be reliably achieved. As a result, this device offers multi- bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short- term memory and long- term memory are emulated across dynamical timescales. Spike- timing- dependent plasticity and paired- pulse facilitation are also demonstrated. These mechanisms are capable of self- pruning to generate efficient neural networks. Time- dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human’s higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.In this study, lithium- doped silicate resistive random access memory with a titanium nitride (TiN) electrode is shown to mimic biological synapses. The TiN electrode effectively stores lithium ions, a principle widely adopted from battery construction, and enables reliable state- dependent decay. This device offers multi- bit functionality and synaptic plasticity, short- term memory and long- term memory, spike- timing- dependent plasticity and paired- pulse facilitation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/3/smll202003964-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/2/smll202003964_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/1/smll202003964.pd

Experimental Study on Electromagnetic Shielding Characteristics of a Fe-Based Amorphous Soft Magnetic Composite

An Fe-based amorphous soft magnetic composite with flexibility and elasticity was fabricated to shield harmful electromagnetic waves in industrial and military defense applications. Through the combination and structural arrangement of the amorphous soft magnetic sheet and the conductive sheet, the inlet (POE) form of electromagnetic waves was artificially diversified, and shielding performance was measured according to the criteria of MIL-STD-188-125-1 in the range from 1 kHz to 10 GHz, in consideration of the electromagnetic pulse (EMP) protection. As a result, the shielding effectiveness of 80 dB was achieved in a triple “sandwiched” structure by alternately stacking an iron-based amorphous soft magnetic material on top of a flexible conductive sheet at a 90-degree angle, rather than in parallel

Reinforcement Learning Based Topology Control for UAV Networks

The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs

Understanding of the Abrupt Resistive Transition in Different Types of Threshold Switching Devices From Materials Perspective

This article proposes a thyristor conduction-insulated gate bipolar transistor (TC-IGBT) with a p-n-p base and an n-p-n collector to reduce turn-off loss. The parasitic p-collector/n-drift/floating p (FP)-layer/carrier stored (CS)-layer thyristor is activated by the double channel gate and the p-n-p base acts a hole barrier to increase hole concentration at the top side. The n-p-n collector is used for extracting electrons from the n-drift region to decrease hole concentration at the bottom side. Therefore, these two effects form linear and descending hole concentration distribution profile. As a result, the p-n-p base and the n-p-n collector in the TC-IGBT offers lower turn-off loss and turn-off fall time. TCAD numerical simulations show reductions up to 47% (3.15 mJ) and 52% (34 ns) in turn-off loss and turn-off fall time, respectively, when compared to a field stop (FS) IGBT with similar breakdown voltage, threshold voltage, and short circuit time. Therefore, this designed structure may be attractive for power electronics applications.11Nsciescopu

Colorimetric detection of penicillin G in milk using antibody-functionalized dendritic platinum nanoparticles

A facile colorimetric method for detecting residual penicillin G in milk using amine-immobilized glass vials and dendritic platinum nanoparticles has been developed. Two distinct methods, liquid-phase and vapor-phase deposition, were used to treat a glass vial with 3-aminopropyl triethoxysilane (APTES) to produce amine groups on the internal surface of the vial. A milk solution spiked with penicillin G was introduced into the glass vial and any compounds in the milk containing carboxylic acid groups (including penicillin G) were allowed to bind to the glass surface through the formation of amide bonds. However, subsequently-added antibody-functionalized dendritic platinum nanoparticles were found to bind only to the penicillin G, which provides the specificity of the proposed assay. Addition of 3,3',5,5'-tetramethylbenzidine (TMB) solution to the glass vial induced color changes owing to the platinum-nanoparticle-mediated oxidation of TMB. The color change could be identified with the naked eye and the limit of detection was found to be 1 ng/mL. The observed color change in the glass vial treated with APTES in the vapor phase was higher than that in the liquid phase, which was attributed to the presence of a higher density of accessible amine groups. (C) 2017 Elsevier B.V. All rights reserved.114sciescopu

ZnO nanorod-coated quartz crystals as self-cleaning thiol sensors for natural gas fuel cells

ZnO nanorods were directly grown on quartz crystals in zinc nitrate hexahydrate Solution. When exposed to hexanethiol vapor, the hexanethiol molecules adsorbed onto the nanorod-coated surface. Subsequent exposure of the adsorbed hexanethiol molecules to UV irradiation led to the photodegradation of the molecules. The adsorption and photocatalytic degradation processes were monitored by measuring changes in the resonant frequency of the crystal using a quartz crystal microbalance (QCM). The frequency change due to the adsorption of hexanethiol molecules Onto the ZnO nanorod-coated quartz crystal was similar to 10 times that observed using a quartz crystal coated with a plain ZnO film, due to the higher Surface area of the nanorods compared to the film. UV irradiation of the nanorod-coated quartz crystals on which hexanethiol was adsorbed caused the frequency to return to the value for the bare ZnO surface indicating that the hexanethiol molecules adsorbed on the ZnO surfaces were completely removed. The complete removal of hexanethiols was confirmed by X-ray photoelectron spectroscopy analysis. The results highlight the potential of ZnO nanorod-grown QCMs as self-cleaning sensors capable of long-term operation Under harsh conditions