Titlebook: Emerging Technology and Architecture for Big-data Analytics; Anupam Chattopadhyay,Chip Hong Chang,Hao Yu Book 2017 Springer International

JOG

Chronological

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

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

衣服

变白

mercenary

Organonitrile

Sinus-Rhythm

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

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