NeuroTest: Testing Solutions for Neuromorphic Circuits and Architectures

NeuroTest：神经形态电路和架构的测试解决方案

• 批准号: 429238884
• 负责人: Professor Mehdi B. Tahoori, Ph.D.
• 金额: --
• 依托单位: Institut für Technische Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/429238884?language=en
• 关键词: NeuroTest; Testing; Solutions; Neuromorphic; Circuits

Deep neural networks (DNNs) are gaining increasing attention and usage in many fields related to artificial intelligence and cognitive processing. Due to challenges associated with the implementation of DNNs using traditional computing architectures, there is a growing interest for brain-inspired, aka Neuromorphic computing platforms and paradigms for direct and hence more efficient implementation of DNNs. The building blocks and operation mode of circuitries and the architectures for Neuromorphic computing bring new challenges, since the implementation and operation of neural networks is fundamentally different from traditional Boolean logic. From the technology point of view, they are based on emerging non-volatile resistive memories, which have new fabrication processes and steps, and hence subject to new types of defects and failures. At the circuit-level, they use synapses and neurons as the building blocks; which are mostly analog or contain many analog components (to store weights, and process firing functions) so there is no clear distinction between analog and digital components. They may also work on analog inputs, such as spiking inputs, as used in spiking neural networks. The non-volatile resistive memory devices perform both storage and logical operations (aggregation function), so there is no clear separation of memory storage and logic blocks. From the functional perspective, unlike Boolean logic in which correct digital value of all output bits are required during all operational cycles, neural networks have inherent inaccuracy and non-determinism, which means they have intrinsic tolerance to faults. Both training and inference phases are inherently imperfect and contains some errors. Given these fundamental challenges, in technology, circuitry and functionality of neural networks and neuromorphic implementations compared to traditional digital circuits and architectures, it requires fundamentally new approaches in testing, test pattern generation and Design for Test (DfT) requirements and methods for neural networks and Neuromorphic circuits. In the new philosophy of testing as needed for Neuromorphic circuits, the traditional boundaries between digital and analog test, memory and logic test, structural and functional test do not longer exist. The purpose of this proposal is to develop proper fault models, design for test, and test generation schemes for Neuromorphic circuits and architectures to tackle these challenges.

深度神经网络（DNN）在与人工智能和认知处理相关的许多领域中越来越受到关注和使用。由于与使用传统计算架构实现DNN相关联的挑战，人们对大脑启发的（也称为神经形态计算平台和范例）越来越感兴趣，以直接并因此更有效地实现DNN。由于神经网络的实现和操作与传统的布尔逻辑有着根本的不同，因此神经形态计算的电路和架构的构建块和操作模式带来了新的挑战。从技术的角度来看，它们基于新兴的非易失性电阻存储器，其具有新的制造工艺和步骤，因此会出现新类型的缺陷和故障。 在电路级，它们使用突触和神经元作为构建块;它们大多是模拟的或包含许多模拟组件（用于存储权重和处理触发函数），因此模拟和数字组件之间没有明确的区别。它们也可以在模拟输入上工作，例如尖峰输入，如尖峰神经网络中使用的。非易失性电阻存储器设备执行存储和逻辑操作（聚合功能）两者，因此不存在存储器存储和逻辑块的明确分离。 从功能的角度来看，与布尔逻辑不同，在布尔逻辑中，所有输出位的正确数字值都需要在所有操作周期中，神经网络具有固有的不准确性和非确定性，这意味着它们具有内在的容错性。 训练和推理阶段都是不完美的，并且包含一些错误。考虑到这些基本挑战，与传统数字电路和架构相比，神经网络和神经形态实现的技术、电路和功能，它需要在测试、测试模式生成和用于神经网络和神经形态电路的测试设计（DfT）要求和方法方面的全新方法。在神经形态电路所需测试的新理念中，数字和模拟测试、存储器和逻辑测试、结构和功能测试之间的传统界限不再存在。本建议的目的是开发适当的故障模型，测试设计，测试生成方案的神经形态电路和架构，以应对这些挑战。