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Register a new userBottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial Intelligence
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Added by
Charlotte Frenkel
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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While Moores law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of new alternative brain-inspired computing architectures that promise to achieve the flexibility and computational efficiency of biological neural processing systems. Within this context, neuromorphic intelligence represents a paradigm shift in computing based on the implementation of spiking neural network architectures tightly co-locating processing and memory. In this paper, we provide a comprehensive overview of the field, highlighting the different levels of granularity present in existing silicon implementations, comparing approaches that aim at replicating natural intelligence (bottom-up) versus those that aim at solving practical artificial intelligence applications (top-down), and assessing the benefits of the different circuit design styles used to achieve these goals. First, we present the analog, mixed-signal and digital circuit design styles, identifying the boundary between processing and memory through time multiplexing, in-memory computation and novel devices. Next, we highlight the key tradeoffs for each of the bottom-up and top-down approaches, survey their silicon implementations, and carry out detailed comparative analyses to extract design guidelines. Finally, we identify both necessary synergies and missing elements required to achieve a competitive advantage for neuromorphic edge computing over conventional machine-learning accelerators, and outline the key elements for a framework toward neuromorphic intelligence.
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This paper introduces a novel all-spike low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs). In the proposed system, event-driven neuromorphic sensors produce asynchronous time-encoded data streams that are encoded by an SNN, whose output spiking signals are pulse modulated via IR and transmitted over general frequence-selective channels; while the receivers inputs are obtained via hard detection of the received signals and fed to an SNN for classification. We introduce an end-to-end training procedure that treats the cascade of encoder, channel, and decoder as a probabilistic SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC). The proposed system, termed NeuroJSCC, is compared to conventional synchronous frame-based and uncoded transmissions in terms of latency and accuracy. The experiments confirm that the proposed end-to-end neuromorphic edge architecture provides a promising framework for efficient and low-latency remote sensing, communication, and inference.
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This article reviews the Once learning mechanism that was proposed 23 years ago and the subsequent successes of One-shot learning in image classification and You Only Look Once - YOLO in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.
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