Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Accelerating Deep Convolutional Neural Networks Using Specialized Hardware
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
Accelerating Convolutional Neural Networks for Mobile Applications
A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks
DrAcc: a DRAM based Accelerator for Accurate CNN Inference
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip
SqueezeNext: Hardware-Aware Neural Network Design
A High Efficiency Accelerator for Deep Neural Networks
F-CNN: An FPGA-based framework for training Convolutional Neural Networks
SpWA: An Efficient Sparse Winograd Convolutional Neural Networks Accelerator on FPGAs
PLACID: A Platform for FPGA-Based Accelerator Creation for DCNNs
A Locality Aware Convolutional Neural Networks Accelerator
A Convolutional Neural Network Fully Implemented on FPGA for Embedded Platforms
Deep Convolutional Neural Network Architecture With Reconfigurable Computation Patterns
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
From high-level deep neural models to FPGAs
DeepSense: A GPU-based Deep Convolutional Neural Network Framework on Commodity Mobile Devices
Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
An automatic RTL compiler for high-throughput FPGA implementation of diverse deep convolutional neural networks
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
Precise deep neural network computation on imprecise low-power analog hardware.
In vitro antibacterial activity of some plant essential oils
Communication via extracellular vesicles enhances viral infection of a cosmopolitan alga
An empirical evaluation of deep architectures on problems with many factors of variation
Sentiment-specific word embedding for Indonesian sentiment analysis