Security of Systems on Chip

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In recent years, technology has started to evolve to become more power efficient, powerful in terms of processors and smaller in size. This evolution of electronics has led microprocessors and other components to be merged to form a circuit called System-on-Chip. If we are to make a vast and cursory comparison between SoC and microcontrollers, microprocessors, and CPUs; we would come to the conclusion of SoCs being a single chip, doing all the things the other components can do yet without needing any external parts. So SoCs are computers just by themselves. Furthermore, SoCs have more memory than microcontrollers in general. Being a computer just by themselves allows them also to become servers. Nowadays, an SoC may be regarded also as a Server-on-Chi

Analyzing multiple conflicts in SAT: an experimental evaluation

Unit propagation and conflict analysis are two essential ingredients of CDCL SAT Solving. The order in which unit propagation is computed does not matter when no conflict is found, because it is well known that there exists a unique unit-propagation fixpoint. However, when a conflict is found, current CDCL implementations stop and analyze that concrete conflict, even though other conflicts may exist in the unit-propagation closure. In this experimental evaluation, we report on our experience in modifying this concrete aspect in the CaDiCaL SAT Solver and try to answer the question of whether we can improve the performance of SAT Solvers by the analysis of multiple conflicts.All authors are supported by grant PID2021-122830OB-C43, funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF: A way of making Europe”Peer ReviewedPostprint (published version

A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning

The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous ONN architectures are mainly designed for general matrix multiplication (GEMM), leading to unnecessarily large area cost and high control complexity. Here, we move beyond classical GEMM-based ONNs and propose an optical subspace neural network (OSNN) architecture, which trades the universality of weight representation for lower optical component usage, area cost, and energy consumption. We devise a butterfly-style photonic-electronic neural chip to implement our OSNN with up to 7x fewer trainable optical components compared to GEMM-based ONNs. Additionally, a hardware-aware training framework is provided to minimize the required device programming precision, lessen the chip area, and boost the noise robustness. We experimentally demonstrate the utility of our neural chip in practical image recognition tasks, showing that a measured accuracy of 94.16% can be achieved in hand-written digit recognition tasks with 3-bit weight programming precision.Comment: 17 pages,5 figure

Synchrotron X-Ray Microdiffraction Investigation of Scaling Effects on Reliability for Through-Silicon Vias for 3-D Integration

Synchrotron x-ray microdiffraction has been applied to TSV characterization in various studies for nondestructive inspection with submicron resolution due to its high beam intensity and penetration depth. In this paper, the application of this technique to TSV investigations is examined and the correlation of the plastic deformation to the microstructure and extrusion behavior along with the effect of TSV dimensional scaling is examined. It is shown that the variability of the copper microstructure and resulting TSV behavior requires a larger number of samples in order to report statistically significant observations. The role of the microstructure in creating statistical scatter is demonstrated through microdiffraction measurements of grain orientation correlated with the observed peak widening, which shows that degraded TSV reliability is largely due to the high elastic anisotropy of copper. After taking the statistical variations into account, the scaling effect was clearly observed, with larger plastic deformation in 2μm diameter TSVs than in 5μm diameter TSVs consistent with microstructure variations. This is confirmed by TSV extrusion measurements, which show that the magnitude and statistical spread of the via extrusion for the 2μm diameter TSVs is higher than that of the 5μm diameter TSVs. These results, validated by thermomechanical simulation, demonstrate first that large sample sizes are required in copper TSV investigations due to high variability, which is not improved with scaling