Titlebook: Applied Reconfigurable Computing. Architectures, Tools, and Applications; 19th International S Francesca Palumbo,Georgios Keramidas,Pedro C

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https://doi.org/10.1007/978-981-15-7493-1that decide the order of running tasks to improve the system’s performance: memory model-aware (MMA) and memory access pattern-aware (MAPA) schedulers. The proposed approaches consider memory characteristics in scheduling decisions to alleviate the memory overhead and enhance the system’s performanc

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Anoop Singh,Shaili Srivastava,Deepak Pantrithm is a statistical approximate computing method, allowing for efficient parallelization. We enhanced the BLB algorithm with a streaming mode to neglect data storage and memory transfer overhead. This allows us to take full advantage of the hardware capabilities of FPGAs. We supersede resampling

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Ashutosh Kumar Choudhary,Parveen Kumar. In this paper, we investigate a dataflow of dataflows architecture that optimizes data access and processing element utilization. The architecture is described with high-level synthesis and offers multiple configuration options including varying the number of independent hardware threads, the inte

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Environmental Microbiology and Biotechnologyticular, our attack targets the widely-used Versatile Tensor Accelerator (VTA). A hardware trojan is employed to track the memory transactions by monitoring the AXI interface signals of VTA’s submodules. The memory side-channel information is leaked through a UART port, which reveals the DNN archite

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Ecology of Microorganisms in Freshwaterontinuous progress on generational technologies (3G, 4G, 5G, and more recently 6G). To facilitate smooth deployments on multi-generation environments, communication systems have to be flexible, i.e., being able to support more than one security mechanisms. In this article, two multimode architecture

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https://doi.org/10.1007/978-94-007-1460-1l decoding. However, to function in real-time on portable devices, these algorithms must adhere to stringent limitations on computational power and memory. In this work, we exploit spiking neural networks (SNNs) within a real-time neural decoding system deployed on a low-end Artix-7 FPGA. The system

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