Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Energy-performance design exploration of a low-power microprogrammed deep-learning accelerator
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs
Exploiting simple analytical models for modeling hardware accelerators
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
DrAcc: a DRAM based Accelerator for Accurate CNN Inference
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
XPPE: cross-platform performance estimation of hardware accelerators using machine learning
YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration
Regression modeling strategies for microarchitectural performance and power prediction
A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures
AEP: An error-bearing neural network accelerator for energy efficiency and model protection
End-to-End Learning of Energy-Constrained Deep Neural Networks.
Dynamically Adaptable Pipeline for Energy-Efficient Microarchitectures Under Wide Voltage Scaling
Accurate and efficient regression modeling for microarchitectural performance and power prediction
Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAs
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?
SparseNN: An energy-efficient neural network accelerator exploiting input and output sparsity
towards efficient microarchitectural design for accelerating unsupervised gan - based deep learning .
COSY: An Energy-Efficient Hardware Architecture for Deep Convolutional Neural Networks Based on Systolic Array
RENO: A high-efficient reconfigurable neuromorphic computing accelerator design
Roofline: an insightful visual performance model for multicore architectures
26th annual computational neuroscience meeting (CNS*2017): part 3
AmiGO: online access to ontology and annotation data
CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling
Identifying patent infringement using SAO based semantic technological similarities