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Found 7 papers, 2 papers with code
Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match.
A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture
This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots.
Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune
This makes the analog to digital converter (ADC) critical in CIM architectures.
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training
Our prior work (DNN+NeuroSim V1. 1) was developed to estimate the impact of reliability in synaptic devices, and analog-to-digital converter (ADC) quantization loss on the accuracy and hardware performance of inference engines.
High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS
In this work, we demonstrate a scalable RRAM based in-memory computing design, termed XNOR-RRAM, which is fabricated in a 90nm CMOS technology with monolithic integration of RRAM devices between metal 1 and 2.
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
Device and System Level Design Considerations for Analog-Non-Volatile-Memory Based Neuromorphic Architectures
This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses.
