Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential ...role in enabling the brain-inspired spiking neural networks (SNNs) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer SNN trained with an unsupervised spike-timing-dependent plasticity (STDP) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations (SOPs), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that TTFS coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. TTFS coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer SOPs than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs' constraints and considerations in neuromorphic systems.
We propose a spectrometer-free refractive index sensor based on a graphene plasmonic structure. The spectrometer-free feature of the device is realized thanks to the dynamic tunability of graphene's ...chemical potential, through electrostatic biasing. The proposed sensor exhibits a 1566 nm/RIU sensitivity, a 250.6 RIU
figure of merit in the optical mode of operation and a 713.2 meV/RIU sensitivity, a 246.8 RIU
figure of merit in the electrical mode of operation. This performance outlines the optimized operation of this spectrometer-free sensor that simplifies its design and can bring terahertz sensing one step closer to its practical realization, with promising applications in biosensing and/or gas sensing.
The successful development of modern gas sensing technologies requires high sensitivity and selectivity coupled to cost effectiveness, which implies the necessity to miniaturize devices while ...reducing the amount of sensing material. The appealing alternative of integrating nanoparticles of a porous metal–organic framework (MOF) onto capacitive sensors based on interdigitated electrode (IDE) chips is presented. We report the deposition of MIL-96(Al) MOF thin films via the Langmuir–Blodgett (LB) method on the IDE chips, which allowed the study of their gas/vapor sensing properties. First, sorption studies of several organic vapors like methanol, toluene, chloroform, etc. were conducted on bulk MOF. The sorption data revealed that MIL-96(Al) presents high affinity toward water and methanol. Later on, ordered LB monolayer films of MIL-96(Al) particles of ∼200 nm were successfully deposited onto IDE chips with homogeneous coverage of the surface in comparison to conventional thin film fabrication techniques such as drop-casting. The sensing tests showed that MOF LB films were selective for water and methanol, and short response/recovery times were achieved. Finally, chemical vapor deposition (CVD) of a porous thin film of Parylene C (thickness ∼250–300 nm) was performed on top of the MOF LB films to fabricate a thin selective layer. The sensing results showed an increase in the water selectivity and sensitivity, while those of methanol showed a huge decrease. These results prove the feasibility of the LB technique for the fabrication of ordered MOF thin films onto IDE chips using very small MOF quantities.
The increase in demand and popularity of smart textiles brings new and innovative ideas to develop a diverse range of textile-based devices for our daily life applications. Smart textile-based ...sensors (TEX sensors) become attractive due to the potential to replace current solid-state sensor devices with flexible and wearable devices. We have developed a smart textile sensor for humidity detection using a metal–organic framework (MOF) as an active thin-film layer. We show for the first time the use of the Langmuir–Blodgett (LB) technique for the deposition of a MIL-96(Al) MOF thin film directly onto the fabrics containing interdigitated textile electrodes for the fabrication of a highly selective humidity sensor. The humidity sensors were made from two different types of textiles, namely, linen and cotton, with the linen-based sensor giving the best response due to better coverage of MOF. The TEX sensor showed a reproducible response after multiple cycles of measurements. After 3 weeks of storage, the sensor showed a moderate decrease in response. Moreover, TEX sensors showed a high level of selectivity for the detection of water vapors in the presence of several volatile organic compounds (VOCs). Interestingly, the selectivity is superior to some of the previously reported MOF-coated solid-state interdigitated electrode devices and textile sensors. The method herein described is generic and can be extended to other textiles and coating materials for the detection of toxic gases and vapors.
The promise of oceanic discovery has long intrigued scientists and explorers, whether with the idea of studying underwater ecology and climate change or with the hope of uncovering natural resources ...and historic secrets buried deep in archaeological sites. This quest to explore the oceans requires skilled human access, yet much of the oceans are inaccessible to human divers; nearly ninetenths of the ocean floor is at 1 km or deeper 1. Accessing these depths is imperative since factors such as pollution and deep-sea trawling threaten ecology and archaeological sites. While remotely operated vehicles (ROVs) are inadequate for the task, a robotic avatar could go where humans cannot and still embody human intelligence and intentions through immersive interfaces.
Topological phases of matter are conventionally characterized by the bulk‐boundary correspondence in Hermitian systems. The topological invariant of the bulk in d dimensions corresponds to the number ...of (d − 1)‐dimensional boundary states. By extension, higher‐order topological insulators reveal a bulk‐edge‐corner correspondence, such that nth order topological phases feature (d − n)‐dimensional boundary states. The advent of non‐Hermitian topological systems sheds new light on the emergence of the non‐Hermitian skin effect (NHSE) with an extensive number of boundary modes under open boundary conditions. Still, the higher‐order NHSE remains largely unexplored, particularly in the experiment. An unsupervised approach—physics‐graph‐informed machine learning (PGIML)—to enhance the data mining ability of machine learning with limited domain knowledge is introduced. Through PGIML, the second‐order NHSE in a 2D non‐Hermitian topoelectrical circuit is experimentally demonstrated. The admittance spectra of the circuit exhibit an extensive number of corner skin modes and extreme sensitivity of the spectral flow to the boundary conditions. The violation of the conventional bulk‐boundary correspondence in the second‐order NHSE implies that modification of the topological band theory is inevitable in higher dimensional non‐Hermitian systems.
Physics‐graph‐informed machine learning (PGIML), formulated by integrating physical principles, graph visualization, and machine learning, enables the identification of the second‐order non‐Hermitian skin effect in topological circuits and thus opens new avenues for understanding the interplay with higher‐order topology. PGIML establishes a powerful methodology for the description of extremely complex systems.
Many emerging technologies have the potential to improve health care by providing more personalized approaches or early diagnostic methods. In this review, we cover smartphone-based multiplexed ...sensors as affordable and portable sensing platforms for point-of-care devices. Multiplexing has been gaining attention recently for clinical diagnosis considering certain diseases require analysis of complex biological networks instead of single-marker analysis. Smartphones offer tremendous possibilities for on-site detection analysis due to their portability, high accessibility, fast sample processing, and robust imaging capabilities. Straightforward digital analysis and convenient user interfaces support networked health care systems and individualized health monitoring. Detailed biomarker profiling provides fast and accurate analysis for disease diagnosis for limited sample volume collection. Here, multiplexed smartphone-based assays with optical and electrochemical components are covered. Possible wireless or wired communication actuators and portable and wearable sensing integration for various sensing applications are discussed. The crucial features and the weaknesses of these devices are critically evaluated.
Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation ...through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on event-based datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Thus, the proposed method has shown high potential to enable fast and energy-efficient on-chip training for real-time learning at the edge.
To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high ...energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve energy efficiency. The adaptive pruning method explores neural dynamics and firing activity of SNNs and adapts the pruning threshold over time and neurons during training. The proposed adaptation scheme allows the network to effectively identify critical weights associated with each neuron by changing the pruning threshold dynamically over time and neurons. It balances the connection strength of neurons with the previous layer with adaptive thresholds and prevents weak neurons from failure after pruning. We also evaluated improvement in the energy efficiency of SNNs with our method by computing synaptic operations (SOPs). Simulation results and detailed analyses have revealed that applying adaptation in the pruning threshold can significantly improve network performance and reduce the number of SOPs. The pruned SNN with 800 excitatory neurons can achieve a 30% reduction in SOPs during training and a 55% reduction during inference, with only 0.44% accuracy loss on MNIST dataset. Compared with a previously reported online soft pruning method, the proposed adaptive pruning method shows 3.33% higher classification accuracy and 67% more reduction in SOPs. The effectiveness of our method was confirmed on different datasets and for different network sizes. Our evaluation showed that the implementation overhead of the adaptive method regarding speed, area, and energy is negligible in the network. Therefore, this work offers a promising solution for effective network compression and building highly energy-efficient neuromorphic systems in real-time applications.
The inevitable variability within electronic devices causes strict constraints on operation, reliability and scalability of the circuit design. However, when a compromise arises among the different ...performance metrics, area, time and energy, variability then loosens the tight requirements and allows for further savings in an alternative design scope. To that end, unconventional computing approaches are revived in the form of approximate computing, particularly tuned for resource-constrained mobile computing. In this paper, a proof-of-concept of the approximate computing paradigm using memristors is demonstrated. Stochastic memristors are used as the main building block of probabilistic logic gates. As will be shown in this paper, the stochasticity of memristors' switching characteristics is tightly bound to the supply voltage and hence to power consumption. By scaling of the supply voltage to appropriate levels stochasticity gets increased. In order to guide the design process of approximate circuits based on memristors a realistic device model needs to be elaborated with explicit emphasis of the probabilistic switching behavior. Theoretical formulation, probabilistic analysis, and simulation of the underlying logic circuits and operations are introduced. Moreover, the expected output behavior is verified with the experimental measurements of valence change memory cells. Hence, it is shown how the precision of the output is varied for the sake of the attainable gains at different levels of available design metrics. This approach represents the first proposition along with physical verification and mapping to real devices that combines stochastic memristors into unconventional computing approaches.
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