JOG 发表于 2025-3-26 22:23:28
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Compute-in-Memory Architecture for Data-Intensive Kernelschnology scaling are unlikely to sufficiently improve energy-efficiency. This chapter describes two embodiments of a novel and reconfigurable memory-based computing architecture which is designed to handle data-intensive kernels in a scalable and energy-efficient manner, suitable for next-generation systems.Comedienne 发表于 2025-3-27 06:32:46
Ultra-Low-Power Biomedical Circuit Design and Optimization: Catching the Don’t Caresiomedical circuit design and optimization. The proposed framework seamlessly integrates data processing algorithms and their customized circuit implementations for co-optimization. The efficacy of the proposed framework is demonstrated by a case study of brain–computer interface (BCI).RACE 发表于 2025-3-27 11:46:51
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Least-squares-solver Based Machine Learning Accelerator for Real-time Data Analytics in Smart Buildi machine-learning accelerator for real-time data analytics in smart micro-grid of buildings. A compact yet fast incremental least-squares-solver based learning algorithm is developed on computational resource limited IoT hardware. The compact accelerator mapped on FPGA can perform real-time data ana圆木可阻碍 发表于 2025-3-28 13:17:55
Compute-in-Memory Architecture for Data-Intensive Kernelssing large datasets. These . kernels differentiate themselves from . kernels in that increased processor performance through parallel execution and technology scaling are unlikely to sufficiently improve energy-efficiency. This chapter describes two embodiments of a novel and reconfigurable memory-b