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- W3205200804 abstract "Extreme classification (XC) is the essential component of large-scale Deep Learning Systems for a wide range of application domains, including image recognition, language modeling, and recommendation. As classification categories keep scaling in real-world applications, the classifier’s parameters could reach several thousands of Gigabytes, way exceed the on-chip memory capacity. With the advent of near-memory processing (NMP) architectures, offloading the XC component onto NMP units could alleviate the memory-intensive problem. However, naive NMP design with limited area and power budget cannot afford the computational complexity of full classification. To tackle the problem, we first propose a novel screening method to reduce the computation and memory consumption by efficiently approximating the classification output and identifying a small portion of key candidates that require accurate results. Then, we design a new extreme-classification-tailored NMP architecture, namely ENMC, to support both screening and candidates-only classification. Overall, our approximate screening method achieves 7.3 × speedup over the CPU baseline, and ENMC further improves the performance by 7.4 × and demonstrates 2.7 × speedup compared with the state-of-the-art NMP baseline." @default.
- W3205200804 created "2021-10-25" @default.
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- W3205200804 date "2021-10-17" @default.
- W3205200804 modified "2023-09-25" @default.
- W3205200804 title "ENMC: Extreme Near-Memory Classification via Approximate Screening" @default.
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- W3205200804 doi "https://doi.org/10.1145/3466752.3480090" @default.
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