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1. Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

   https://doi.org/10.1038/s41467-022-32078-6  

   Kireev, Dmitry; Liu, Samuel; Jin, Harrison; Patrick Xiao, T.; Bennett, Christopher H.; Akinwande, Deji; Incorvia, Jean Anne C. (July 2022, Nature Communications)

   Abstract CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm²switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks. 

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2. PARAG: PIM Architecture for Real-Time Acceleration of GCNs

   https://doi.org/10.1109/HiPC58850.2023.00016  

   Singh, Gian; Kuppannagari, Sanmukh R; Vrudhula, Sarma (December 2023, IEEE)

   Graph Convolutional Networks (GCNs) have successfully incorporated deep learning to graph structures for social network analysis, bio-informatics, etc. The execution pattern of GCNs is a hybrid of graph processing and neural networks which poses unique and significant challenges for hardware implementation. Graph processing involves a large amount of irregular memory access with little computation whereas processing of neural networks involves a large number of operations with regular memory access. Existing graph processing and neural network accelerators are therefore inefficient for computing GCNs. This paper presents Parag, processing in memory (PIM) architecture for GCN computation. It consists of customized logic with minuscule computing units called Neural Processing Elements (NPEs) interfaced to each bank of the DRAM to support parallel graph processing and neural network computation. It utilizes the massive internal parallelism of DRAM to accelerate the GCN execution with high energy efficiency. Simulation results for inference of GCN over standard datasets show a latency and energy reduction by three orders of magnitude over a CPU implementation. When compared to a state-of-the-art PIM architecture, PARAG achieves on an average 4x reduction in latency and 4.23x reduction in the energy-delay-product (EDP). 

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3. Real-time multi-task diffractive deep neural networks via hardware-software co-design

   https://doi.org/10.1038/s41598-021-90221-7  

   Li, Yingjie; Chen, Ruiyang; Sensale-Rodriguez, Berardi; Gao, Weilu; Yu, Cunxi (December 2021, Scientific Reports)

   null (Ed.)

   Abstract Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D 2 NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables first-of-its-like real-time multi-task learning in D 2 2NNs that automatically recognizes which task is being deployed in real-time. Our experimental results demonstrate significant improvements in versatility, hardware efficiency, and also demonstrate and quantify the robustness of proposed multi-task D 2 NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task. 

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4. The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent

   Sankararaman, Karthik A; De, Soham; Xu, Zheng; Huang, Ronny; Goldstein, Tom (March 2020, International Conference on Machine Learning)

   This paper studies how neural network architecture affects the speed of training. We introduce a simple concept called gradient confusion to help formally analyze this. When gradient confusion is high, stochastic gradients produced by different data samples may be negatively correlated, slowing down convergence. But when gradient confusion is low, data samples interact harmoniously, and training proceeds quickly. Through theoretical and experimental results, we demonstrate how the neural network architecture affects gradient confusion, and thus the efficiency of training. Our results show that, for popular initialization techniques, increasing the width of neural networks leads to lower gradient confusion, and thus faster model training. On the other hand, increasing the depth of neural networks has the opposite effect. Our results indicate that alternate initialization techniques or networks using both batch normalization and skip connections help reduce the training burden of very deep networks. 

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5. A spiking neural network with continuous local learning for robust online brain machine interface

   https://doi.org/10.1088/1741-2552/ad1787  

   Taeckens, Elijah A.; Shah, Sahil (January 2024, Journal of Neural Engineering)

   Abstract Objective.Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and they cannot be trained on a continuous input stream without pausing periodically to perform backpropagation. An ideal BMI should be capable training continuously without interruption to minimize disruption to the user and adapt to changing neural environments.Approach.We propose a continuous SNN weight update algorithm that can be trained to perform regression learning with no need for storing past spiking events in memory. As a result, the amount of memory needed for training is constant regardless of the input duration. We evaluate the accuracy of the network on recordings of neural data taken from the premotor cortex of a primate performing reaching tasks. Additionally, we evaluate the SNN in a simulated closed loop environment and observe its ability to adapt to sudden changes in the input neural structure.Main results.The continuous learning SNN achieves the same peak correlation ( $\rho =0.7$) as existing SNN training methods when trained offline on real neural data while reducing the total memory usage by 92%. Additionally, it matches state-of-the-art accuracy in a closed loop environment, demonstrates adaptability when subjected to multiple types of neural input disruptions, and is capable of being trained online without any prior offline training.Significance.This work presents a neural decoding algorithm that can be trained rapidly in a closed loop setting. The algorithm increases the speed of acclimating a new user to the system and also can adapt to sudden changes in neural behavior with minimal disruption to the user. 

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