发表机构
Galois Inc; Univ. of London（Galois公司; 伦敦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对嵌入式系统上复杂算法实现方案的不足，提出实时、动态且合理的量化方法及支持硬件，利用从左到右算术和脉动阵列，能在边缘实现资源高效的神经网络与人工智能，确保关键位抗位翻转攻击。
AI 中文摘要
复杂算法如深度神经网络越来越多地部署在嵌入式、资源受限平台上。现有在边缘实现这些模型的硬件和软件方案存在不足，硬件易受故障注入攻击，软件方案要么静态且合理但功耗非最优，要么动态却不合理。为此提出全新实时、动态且合理的量化方法及支持硬件。开发了合理的实时自适应精度量化方法，利用从左到右算术先传递最高有效位，动态调整精度并进行灵敏度分析。还提出利用脉动阵列执行从左到右算术以先生成最高有效位的硬件方法。这为在边缘实现资源高效的神经网络和人工智能，以及在硬件上实现广泛合理且资源高效的高精度数学提供了全新方案，确保对最关键位的位翻转攻击具有弹性。本文展示的是进展中的工作，软件实现已完成，硬件正在进行中。
英文摘要
Complex algorithms such as deep neural networks are increasingly being deployed on embedded, resource constrained platforms. However, existing hardware and software schemes for implementing these models on the edge fall short, particularly for safety-critical applications such as medical devices. First, hardware such as GPUs, NPUs and TPUs are designed for throughput rather than correctness of computation of security, and are as such susceptible to fault injection attacks. Second, software schemes designed for porting algorithms onto edge devices -- such as quantization schemes -- are either static and sound (non-optimal power consumption), or dynamic yet unsound (non-optimal for safety-critical applications). To address both these needs we propose a both wholly new approach to real-time, dynamic and sound quantization, as well as the hardware to support it. First we developed a sound, real-time adaptive-precision quantization approach utilizing left-to-right arithmetic to pass the most significant bits (MSB) first, and dynamically adjust precision online while performing sensitivity analysis to quantify and manage the risk of decision-boundary crossings. Next, we propose a novel hardware approach utilizing systolic arrays to perform left-to-right arithmetic to generate the MSB first. Together this provides a wholly novel scheme for enabling not only resource-efficient neural networks and artificial intelligence at the edge, but broadly sound and resource-efficient high-precision mathematics on hardware that ensures resilience to bit flip attacks on the most critical bits. This is presented herein as work-in-progress, with software implementations completed and hardware in-progress.
