首页|Tampere University Researcher Focuses on Machine Learning (High- Efficiency Compressor Trees for Latest AMD FPGAs)
Tampere University Researcher Focuses on Machine Learning (High- Efficiency Compressor Trees for Latest AMD FPGAs)
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Current study results on artificial intelligence have been published. According to news reporting from Dresden, Germany, by NewsRx journalists, research stated, “High-fan-in dot product computations are ubiquitous in highly relevant application domains, such as signal processing and machine learning.” Our news editors obtained a quote from the research from Tampere University: “Particularly, the diverse set of data formats used in machine learning poses a challenge for flexible efficient design solutions. Ideally, a dot product summation is composed from a carry-free compressor tree followed by a terminal carrypropagate addition. On FPGA, these compressor trees are constructed from generalized parallel counters (GPCs) whose architecture is closely tied to the underlying reconfigurable fabric. This work reviews known counter designs and proposes new ones in the context of the new AMD Versal™ fabric. On this basis, we develop a compressor generator featuring variable-sized counters, novel counter composition heuristics, explicit clustering strategies, and case-specific optimizations like logic gate absorption.”
Tampere UniversityDresdenGermanyEuropeCyborgsEmerging TechnologiesMachine Learning
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