The advent of quantum computing threatens to break many classical cryptographic schemes, leading to innovations in public key cryptography that focus on post-quantum cryptography primitives and protocols resistant to quantum computing threats. Lattice-based cryptography is a promising post-quantum cryptography family, both in terms of foundational properties as well as in its application to both traditional and emerging security problems such as encryption, digital signature, key exchange, and homomorphic encryption. While such techniques provide guarantees, in theory, their realization on contemporary computing platforms requires careful design choices and tradeoffs to manage both the diversity of computing platforms (e.g., high-performance to resource constrained), as well as the agility for deployment in the face of emerging and changing standards. In this work, we survey trends in lattice-based cryptographic schemes, some recent fundamental proposals for the use of lattices in computer security, challenges for their implementation in software and hardware, and emerging needs for their adoption. The survey means to be informative about the math to allow the reader to focus on the mechanics of the computation ultimately needed for mapping schemes on existing hardware or synthesizing part or all of a scheme on special-purpose har dware.

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In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.To approach the subject, we start by introducing trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems.

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As technology scales further, multicore and many-core processors emerge as an alternative to keep up with performance demands. However, because of power and thermal constraints, we are obliged to power off remarkable area of chip. Many innovative techniques have been presented to improve energy efficiency and maintain utilization at the highest level. In this paper, we discuss different models and methods of exploiting dark silicon, and by using dynamic voltage and frequency scaling in Amdahl's law and considering memory overheads, we attempt to decrease amount of dark silicon and improve performance and performance per watt/joule. We propose high-performance and energy-efficient multicore architectures for variety of parallelisms and memory-intensities in workloads. According to the results, by voltage scaling, for a highly parallel CPU-intensive workload, we reach improvements of approximately 5.2× and 3.78× in performance per watt and performance per joule, respectively, while about 27 % reduction of performance should be tolerated. For memory-intensive applications, a negligible change in speedup is detected by scaling, while performance per watt and performance per joule for both serial and parallel applications lead to around 6× enhancements.