3 citations · 5 across the 6 of their papers we have counts for
6 papers
An Efficient General-Purpose Optical Accelerator for Neural Networks
Sijie Fei, Amro Eldebiky, Grace Li Zhang +2
General-purpose optical accelerators (GOAs) have emerged as a promising platform to accelerate deep neural networks (DNNs) due to their low latency and energy consumption. Such an…
BasisN: Reprogramming-Free RRAM-Based In-Memory-Computing by Basis Combination for Deep Neural Networks
Amro Eldebiky, Grace Li Zhang, Xunzhao Yin +4
Deep neural networks (DNNs) have made breakthroughs in various fields including image recognition and language processing. DNNs execute hundreds of millions of multiply-and-accumul…
OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillation
Ruidi Qiu, Amro Eldebiky, Grace Li Zhang +4
Having the potential for high speed, high throughput, and low energy cost, optical neural networks (ONNs) have emerged as a promising candidate for accelerating deep learning tasks…
Class-Aware Pruning for Efficient Neural Networks
Mengnan Jiang, Jingcun Wang, Amro Eldebiky +4
Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for thei…
Analog Feedback-Controlled Memristor programming Circuit for analog Content Addressable Memory
Jiaao Yu, Paul-Philipp Manea, Sara Ameli +3
Recent breakthroughs in associative memories suggest that silicon memories are coming closer to human memories, especially for memristive Content Addressable Memories (CAMs) which…
CorrectNet: Robustness Enhancement of Analog In-Memory Computing for Neural Networks by Error Suppression and Compensation
Amro Eldebiky, Grace Li Zhang, Georg Boecherer +2
The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate (MA…