S-FLASH: A NAND Flash-Based Deep Neural Network Accelerator Exploiting Bit-Level Sparsity | 3 | 0.42 | 2022 |
An Energy-Efficient Deep Convolutional Neural Network Training Accelerator for In Situ Personalization on Smart Devices | 3 | 0.38 | 2020 |
Cremon: Cryptography Embedded On The Convolutional Neural Network Accelerator | 0 | 0.34 | 2020 |
An Energy-Efficient Deep Convolutional Neural Network Inference Processor With Enhanced Output Stationary Dataflow in 65-nm CMOS | 0 | 0.34 | 2020 |
An Energy-efficient Processing-in-memory Architecture for Long Short Term Memory in Spin Orbit Torque MRAM | 1 | 0.36 | 2019 |
A 47.4µJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices | 0 | 0.34 | 2019 |
NAND-Net: Minimizing Computational Complexity of In-Memory Processing for Binary Neural Networks | 7 | 0.46 | 2019 |
eSRCNN: A Framework for Optimizing Super-Resolution Tasks on Diverse Embedded CNN Accelerators | 0 | 0.34 | 2019 |
A PVT-robust Customized 4T Embedded DRAM Cell Array for Accelerating Binary Neural Networks | 0 | 0.34 | 2019 |
NID: processing binary convolutional neural network in commodity DRAM | 2 | 0.37 | 2018 |
TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training | 3 | 0.39 | 2018 |
A Kernel Decomposition Architecture for Binary-weight Convolutional Neural Networks. | 4 | 0.42 | 2017 |
Energy-Efficient Design of Processing Element for Convolutional Neural Network. | 1 | 0.36 | 2017 |
14.6 A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems. | 25 | 1.36 | 2016 |
A 5-Gb/s 2.67-mW/Gb/s Digital Clock and Data Recovery With Hybrid Dithering Using a Time-Dithered Delta-Sigma Modulator. | 1 | 0.37 | 2016 |
Timing error masking by exploiting operand value locality in SIMD architecture | 0 | 0.34 | 2014 |
PowerField: A Probabilistic Approach for Temperature-to-Power Conversion Based on Markov Random Field Theory | 2 | 0.37 | 2013 |
PowerField: a transient temperature-to-power technique based on Markov random field theory | 0 | 0.34 | 2012 |