Abstract. Current embedded multimedia applications have stringent time and power constraints. Coarse-grained reconfigurable processors have been shown to achieve the required performance. However, there is not much research regarding the power consumption of such processors. In this paper, we present a novel coarse-grained reconfigurable processor and study its power consumption using a power model derived from Wattch. Several processor configurations are evaluated using a set of multimedia applications. Results show that the presented coarse-grained processor can achieve on average 2.5x the performance of a RISC processor with an 18% increase in energy consumption.
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Architecture for Dynamically Reconfigurable Embedded Systems (ADRES) is a templatized coarsegrained reconfigurable processor architecture. It targets at embedded applications which demand highperformance, low-power and high-level language programmability. Compared with typical very long instruction word-based digital signal processor, ADRES can exploit higher parallelism by using more scalable hardware with support of novel compilation techniques. We developed a complete tool-chain, including compiler, simulator and HDL generator. This paper describes the design case of a media processor targeting at H.264 decoder and other video tasks based on the ADRES template. The whole processor design, hardware implementaiton and application mapping are done in a relative short period. Yet we obtain C-programmed real-time H.264/AVC CIF decoding at 50 MHz. The die size, clock speed and the power consumption are also very competitive compared with other processors.
Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.
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