Name
Affiliation
Papers
HIROKI NAKAHARA
The authors are with the Department of Computer Science and Electronics, Kyushu Institute of Technology, Iizuka-shi, 820-8502 Japan. E-mail: nakahara@aries01.cse.kyutech.ac.jp, E-mail: sasao@cse.k ...
90
Collaborators
Citations 
PageRank 
80
155
37.34
Referers 
Referees 
References 
354
637
457
Search Limit
100637
Title
Citations
PageRank
Year
Edge Inference Engine for Deep & Random Sparse Neural Networks with 4-bit Cartesian-Product MAC Array and Pipelined Activation Aligner10.372021
A High-Throughput Detection Circuit based on 2 q +1-Valued Deep Neural Networks00.342021
A Novel Compact Planar Magic-T Using Cps And Microstrip-To-Cps Transition00.342021
Energy-Efficient Ecg Signals Outlier Detection Hardware Using A Sparse Robust Deep Autoencoder00.342021
A Low-Latency Inference of Randomly Wired Convolutional Neural Networks on an FPGA00.342021
A Multilayer Perceptron Training Accelerator using Systolic Array00.342021
Weight Sparseness for a Feature-Map-Split-CNN Toward Low-Cost Embedded FPGAs00.342021
Fpga-Based Inter-Layer Pipelined Accelerators For Filter-Wise Weight-Balanced Sparse Fully Convolutional Networks With Overlapped Tiling10.432021
A Table Look-Up Based Ternary Neural Network Processor10.382020
An FPGA-Based Low-Latency Accelerator for Randomly Wired Neural Networks00.342020
2<sup>n</sup>+1-valued SSS-Net: Uniform Shift, Channel Sparseness, and Channel Shuffle00.342020
Optimizing Reconfigurable Recurrent Neural Networks10.392020
R2CNN - Recurrent Residual Convolutional Neural Network on FPGA.00.342020
Tiny On-Chip Memory Realization of Weight Sparseness Split-CNNs on Low-end FPGAs00.342020
A Reconfigurable Multithreaded Accelerator for Recurrent Neural Networks00.342020
Design Method for an LUT Network-Based CNN with a Sparse Local Convolution00.342020
Fast Monocular Depth Estimation on an FPGA00.342020
High-Throughput Convolutional Neural Network on an FPGA by Customized JPEG Compression10.402020
Sentei: Filter-Wise Pruning With Distillation Towards Efficient Sparse Convolutional Neural Network Accelerators00.342020
Guinness: A Gui Based Binarized Deep Neural Network Framework For Software Programmers00.342019
An FPGA-based Fine Tuning Accelerator for a Sparse CNN.10.352019
Filter-Wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation.20.532019
Power Efficient Object Detector With An Event-Driven Camera For Moving Object Surveillance On An Fpga00.342019
Real-Time Multi-Pedestrian Detection in Surveillance Camera using FPGA00.342019
FPGA-Based Training Accelerator Utilizing Sparseness of Convolutional Neural Network10.362019
A Dataflow Pipelining Architecture for Tile Segmentation with a Sparse MobileNet on an FPGA00.342019
An FPGA Implementation of Real-Time Object Detection with a Thermal Camera00.342019
Noise Convolutional Neural Networks and FPGA Implementation00.342019
FPGA-based Accurate Pedestrian Detection with Thermal Camera for Surveillance System00.342019
Many Universal Convolution Cores for Ensemble Sparse Convolutional Neural Networks00.342019
An FPGA Realization of OpenPose Based on a Sparse Weight Convolutional Neural Network10.372018
A Ternary Weight Binary Input Convolutional Neural Network: Realization on the Embedded Processor10.382018
BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W.150.852018
A Performance Per Power Efficient Object Detector On An Fpga For Robot Operating System (Ros)00.342018
A Tri-State Weight Convolutional Neural Network for an FPGA: Applied to YOLOv2 Object Detector00.342018
A Lightweight YOLOv2: A Binarized CNN with A Parallel Support Vector Regression for an FPGA.20.492018
An Fpga Realization Of A Random Forest With K-Means Clustering Using A High-Level Synthesis Design00.342018
A Demonstration of FPGA-Based You Only Look Once Version2 (YOLOv2)00.342018
Power Efficient Object Detector With An Event-Driven Camera On An Fpga00.342018
A Threshold Neuron Pruning For A Binarized Deep Neural Network On An Fpga10.352018
New Generation Dynamically Reconfigurable Processor Technology for Accelerating Embedded AI Applications00.342018
Demonstration of Object Detection for Event-Driven Cameras on FPGAs and GPUs00.342018
An FPGA Realization of a Deep Convolutional Neural Network Using a Threshold Neuron Pruning.10.352017
All binarized convolutional neural network and its implementation on an FPGA30.722017
A Random Forest Using a Multi-valued Decision Diagram on an FPGA20.392017
In-memory area-efficient signal streaming processor design for binary neural networks10.382017
On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA70.742017
A Batch Normalization Free Binarized Convolutional Deep Neural Network on an FPGA (Abstract Only).40.432017
An object detector based on multiscale sliding window search using a fully pipelined binarized CNN on an FPGA00.342017
An FFT Circuit Using Nested RNS in a Digital Spectrometer for a Radio Telescope00.342016
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