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It is well known that there are micro-architectural vulnerabilities that enable an attacker to use caches to exfiltrate secrets from a victim. These vulnerabilities exploit the fact that the attacker can detect cache lines that were accessed by the victim. Therefore, architects have looked at different forms of randomization to thwart the attacker's ability to communicate using the cache. The security analysis of those randomly mapped caches is based upon the increased difficulty for the attacker to determine the addresses that touch the same cache line that the victim has accessed.In this paper, we show that the analyses used to evaluate those schemes were incomplete in various ways. For example, they were incomplete because they only focused on one of the steps used in the exfiltration of secrets. Specifically, the step that the attacker uses to determine the set of addresses that can monitor the cache lines used by the transmitter address. Instead, we broaden the analysis of micro-architecture side channels by providing an overall view of the communication process. This allows us to identify the existence of other communication steps that can also affect the security of randomly mapped caches, but have been ignored by prior work.We design an analysis framework, CaSA, to comprehensively and quantitatively analyze the security of these randomly mapped caches. We comprehensively consider the end-to-end communication steps and study the statistical relationship between different steps. In addition, to perform quantitative analysis, we leverage the concepts from the field of telecommunications to formulate the security analysis into a statistical problem. We use CaSA to evaluate a wide range of attack strategies and cache configurations. Our result shows that the randomization mechanisms used in the state-of-the-art randomly mapped caches are insecure.Index Terms-Micro-architecture side channel, randomly mapped cache, security analysis.

The Bluespec hardware-description language presents a significantly higher-level view than hardware engineers are used to, exposing a simpler concurrency model that promotes formal proof, without compromising on performance of compiled circuits. Unfortunately, the cost model of Bluespec has been unclear, with performance details depending on a mix of user hints and opaque static analysis of potential concurrency conflicts within a design. In this paper we present Kôika, a derivative of Bluespec that preserves its desirable properties and yet gives direct control over the scheduling decisions that determine performance. Kôika has a novel and deterministic operational semantics that uses dynamic analysis to avoid concurrency anomalies. Our implementation includes Coq definitions of syntax, semantics, key metatheorems, and a verified compiler to circuits. We argue that most of the extra circuitry required for dynamic analysis can be eliminated by compile-time BSV-style static analysis.CCS Concepts: • Software and its engineering → Semantics; Compilers; • Hardware → Theorem proving and SAT solving.

Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art planning and control algorithms in robotics. Parallel computing platforms such as GPUs and FPGAs can offer performance gains for algorithms with hardware-compatible computational structures. In this paper, we detail the designs of three faster than state-ofthe-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities. We also find that the relative performance across hardware platforms depends on the number of parallel gradient evaluations required.

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