A Dual-Mode Weight Storage Analog Neural Network Platform for On-Chip Applications

Abstract-On-chip trainable neural networks show great promise in enabling various desired features of modern integrated circuits (IC), such as Built-In Self-Test (BIST), security and trust monitoring, self-healing, etc. Cost-efficient implementation of these features imposes strict area and power constraints on the circuits dedicated to neural networks, which, however, should not compromise their ability to learn fast and retain functionality throughout their lifecycle. To this end, we have designed and fabricated a reconfigurable analog neural network (ANN) chip which serves as an expertise acquisition platform for various applications requiring on-chip ANN integration. With this platform, we intend to address the key cost-efficiency issues: a fully analog implementation with strict area and power budgets, a learning ability of the proposed architecture, fast dynamic programming of the weight memory during training, and high precision non-volatile storage of weight coefficients during operation or standby. We explore two learning structures: a multilayer perceptron (MLP) and an ontogenic neural network with their corresponding training algorithms. The core circuits are biased in weak inversion and make use of the translinear principle for multiplication and non-linear conversion operations. The chip is mounted on a custom PCB and connected to a computer for chip-in-the-loop training. We present measured results of the core circuits and the dual-mode weight memory. The learning ability is evaluated on a 3-input XOR classification task

A Post-Deployment Ic Trust Evaluation Architecture

The use of side-channel parametric measurements along with statistical analysis methods for detecting hardware Trojans in fabricated integrated circuits has been studied extensively in recent years, initially for digital designs but recently also for their analog/RF counterparts. Such post-fabrication trust evaluation methods, however, are unable to detect dormant hardware Trojans which are activated after a circuit is deployed in its field of operation. For the latter, an on-chip trust evaluation method is required. To this end, we present a general architecture for post-deployment trust evaluation based on on-chip classifiers. Specifically, we discuss the design of an on-chip analog neural network which can be trained to distinguish trusted from untrusted circuit functionality based on simple measurements obtained via on-chip measurement acquisition sensors. The proposed method is demonstrated using a Trojan-free and two Trojan-infested variants of a wireless cryptographic IC design, as well as a fabricated programmable neural network experimentation chip. As corroborated by the obtained experimental results, two current measurements suffice for the on-chip classifier to effectively assess trustworthiness and, thereby, detect hardware Trojans that are activated after chip deployment. © 2013 IEEE

Hardware Trojan Detection In Analog/Rf Integrated Circuits

Globalization of semiconductor manufacturing has brought about increasing concerns regarding possible infiltration of the Integrated Circuit (IC) supply chain by skilled and resourceful adversaries, with the intention of introducing malicious modifications (a.k.a hardware Trojans) which can be exploited to cause incorrect results, steal sensitive data, or even incapacitate a chip. While numerous prevention and detection solutions have been introduced in the recent, past, the vast majority of these efforts target digital circuits. Analog/RF ICs, however, are equally vulnerable and potentially even more attractive as attack targets, due to their wireless communication capabilities. Accordingly, in this chapter, we review existing research efforts in hardware Trojan detection in Analog/RF ICs. Specifically, using a wireless cryptographic IC as an experimentation platform, we demonstrate the effectiveness of side-channel fingerprinting along with advanced statistical analysis and machine learning methods in detecting hardware Trojans both after its manufacturing and after its deployment in its field of operation