Recent advances in neural networks (NNs) and their applications in deep learning techniques have made the security aspects of NNs an important and timely topic for fundamental research. In this paper, we survey the security challenges and opportunities in the computing hardware used in implementing deep neural networks (DNN). First, we explore the hardware attack surfaces for DNN. Then, we report the current state-of-the-art hardware-based attacks on DNN with focus on hardware Trojan insertion, fault injection, and side-channel analysis. Next, we discuss the recent development on detecting these hardware-oriented attacks and the corresponding countermeasures. We also study the application of secure enclaves for the trusted execution of NN-based algorithms. Finally, we consider the emerging topic of intellectual property protection for deep learning systems. Based on our study, we find ample opportunities for hardware based research to secure the next generation of DNN-based artificial intelligence and machine learning platforms.

Secure silicon requires a seamless integration of new tools, new IP, and design flows to help designers protect integrated circuits from increasingly sophisticated attacks. Independent Validation and Verification (IV&V) of this integrated technology is important to ensure that the tools actually deliver on their security claims when used by independent parties (i.e., people who were not involved in designing the tools). This work discusses the principles and approaches for IV&V of such a complex design environment, including validation of the security strength of the various hardware security techniques, such as combinational and sequential logic locking, Trojan Detection, side-channel mitigation, and blockchain-based asset management. The main challenge in running an IV&V effort is to ensure that the process provides rigorous, methodical and provable evaluation of the claims of not only the component tools and IP, but whether such an integrated environment can produce security-hardened designs by a non-security expert. CCS Concepts • Hardware $\rightarrow$ Very large scale integration design; Methodologies for EDA; • Security and privacy $\rightarrow$ Security in hardware.

Approximate computing (AC) is an effective energy-efficient method for error-resilient applications. The essence behind AC is to reduce energy consumption by slightly sacrificing computation accuracy purposefully while providing quality-acceptable results. On the other hand, soft error is a common problem during program execution and may cause unacceptable outputs or catastrophic failure to the system. As AC introduces errors and soft errors are mitigated by fault-tolerant mechanisms, they have conflict goals and contradictory approaches. To the best of our knowledge, there is no previous efforts to consider the two at the same time. In this paper, we study the problem of AC with soft errors in order to guarantee the safe execution of the program while reducing energy (by AC). More specifically, we propose FTApprox, a fault-tolerant approximate arithmetic computing data format, to enable the detection and correction of SEs. As an approximate data format, FTApprox can use 16 bits to approximate any 32-bit integers and fixed-point numbers, and will select only the most significant part of operands for AC at runtime. Energy saving is obtained by converting 32-bit arithmetic operations to 8-bit operations. Meanwhile, for soft errors such as random bit flips, FTApprox not only can detect all single bit flips and most 2-bit flips, it can also correct most of these errors. The experimental results show that FTApprox has significant resistance against soft errors while providing 66.4%-79.6% energy saving.

Approximate computing is a promising technique in improving the energy efficiency for error-resilient applications such as multimedia, signal processing and neural network. A large amount of reported work is on the design of approximate computation units with truncated data under error constraints. However, they mainly focus on simple arithmetic operations, addition and multiplication to be more specific. In this paper, we study how to apply the truncation method to the floating-point logarithmic operation which is getting increasingly popular. We analyze the tradeoff between the precision of computation and the energy it requires and derive a formula on the most energy efficient implementation of the logarithm unit for a given error variance range. Based on this theoretical result, we propose BWOLF (Bit-Width optimization for Logarithmic Function), which uses a sequential quadratic programming algorithm to determine the way to truncate data (i.e., bit-width optimization) in a program with logarithm and other arithmetic operations such that the energy consumption is minimized under a fixed error budget. We evaluate the efficacy of BWOLF in energy saving on two widely used applications: Kullback-Leibler Divergence and Bayesian Neural Network. The experimental results validate the correctness of our analysis and show significant amount of energy saving over both the full-precision computation and the uniform truncation method. The energy savings range from 27.18 % to 95.92% for different error constraints.

Approximate computing (AC) is an attractive energy efficient technique that can be implemented at almost all the design levels including data, algorithm, and hardware. The basic idea behind AC is to deliberately control the trade-off between computation accuracy and energy efficiency. However, with the introduction of AC, traditional computing frameworks are having many potential security vulnerabilities. In this paper, we analyze these vulnerabilities and the associated attacks as well as corresponding countermeasures. More importantly, we propose the vulnerability at data level and demonstrate that without appropriate security mechanism, adversaries can modify the data and convert a secure and trusted AC process to one that produces unexpected errors in the final output. Furthermore, it is difficult to distinguish whether such errors are caused by the approximation nature of AC or from malicious modification and injection. Finally, we propose the information hiding based countermeasures to defend against both existing attacks and the proposed data level attacks, which helps to answer the question: given an error in AC, whether it comes from approximation or it is maliciously introduced.

There are many interesting advances in approximate computing recently targeting the energy efficiency in system design and execution. The basic idea is to trade computation accuracy for power and energy during all phases of the computation, from data to algorithm and hardware implementation. In this paper, we explore how to utilize approximate computing for security based information hiding. More specifically, we will demonstrate with examples the potential of embedding information in approximate hardware and approximate data, as well as during approximate computation. We analyze both the security vulnerabilities that this may cause and the potential security applications enabled by such information hiding. We argue that information could be hidden behind approximate computation without compromising the computation accuracy or energy efficiency.