id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
p5547p91s2q	Qiuwen Lou	Cross-Layer Energy Efficient Hardware Design and Benchmarking with CMOS and Emerging Technologies	2020		.txt	text/plain	688	28	33	We applied Eva-DNN on three accelerator architectures from the literature, namely: Eyeriss, ShiDianNao, and TrueNorth and demonstrate that the model can reliably estimate the energy (maximum error of 15%) required by different components of different accelerator architecture topologies for a given workload or network. Specifically, we present (i) the implementation of different layers, including convolution, ReLU, and pooling, in a CoNN using CeNN, (ii) modified CoNN structures with CeNN-friendly layers to reduce computational overheads typically associated with a CoNN, (iii) a mixed-signal CeNN architecture that performs CoNN computations in the analog and mixed signal domain, and (iv) design space exploration that identifies what CeNN-based algorithm and architectural features fare best compared to existing algorithms and architectures when evaluated over common datasets -- MNIST and CIFAR-10.We also focus on benchmarking and evaluating DNN architectures.	cache/p5547p91s2q.txt	txt/p5547p91s2q.txt