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An energy-efficient nonvolatile intelligent processor (NIP) is proposed for battery-less energy harvesting system. This NIP employs RRAM-based nonvolatile logics (NVL) with self-write-termination (SWT) scheme and low-power processing-in-memory (PIM) to achieve energy-efficient computing against frequent power-off situations. An NIP test chip was fabricated in 150nm CMOS process using HfO RRAM. This...
An energy-efficient nonvolatile intelligent processor (NIP) is proposed for battery-less energy harvesting system. This NIP employs RRAM-based nonvolatile logics (NVL) with self-write-termination (SWT) scheme and low-power processing-in-memory (PIM) to achieve energy-efficient computing against frequent power-off situations. An NIP test chip was fabricated in 150nm CMOS process using HfO RRAM. This...
The training of neural network (NN) is usually time-consuming and resource intensive. Memristor has shown its potential in computation of NN. Especially for the metal-oxide resistive random access memory (RRAM), its crossbar structure and multi-bit characteristic can perform the matrix-vector product in high precision, which is the most common operation of NN. However, there exist two challenges on...
An RRAM-based computing system (RCS) is an attractive hardware platform for implementing neural computing algorithms. On-line training for RCS enables hardware-based learning for a given application and reduces the additional error caused by device parameter variations. However, a high occurrence rate of hard faults due to immature fabrication processes and limited write endurance restrict the applicability...
The emerging metal-oxide resistive switching random-access memory (RRAM) devices and RRAM crossbar arrays have demonstrated their potential in enormously boosting the speed and energy-efficiency of analog matrix-vector multiplication. Unfortunately, due to the immature fabrication technology, commonly occurring Stuck-At-Faults (SAFs) seriously degrade the computational accuracy of RRAM crossbar based...
Recent progress in the machine learning field makes low bit-level Convolutional Neural Networks (CNNs), even CNNs with binary weights and binary neurons, achieve satisfying recognition accuracy on ImageNet dataset. Binary CNNs (BCNNs) make it possible for introducing low bit-level RRAM devices and low bit-level ADC/DAC interfaces in RRAM-based Computing System (RCS) design, which leads to faster read-and-write...
Deep learning, and especially Convolutional Neural Network (CNN, is among the most powerful and widely used techniques in computer vision. Applications range from image classification to object detection, segmentation, Optical Character Recognition (OCR), etc. At the same time, CNNs are both computationally intensive and memory intensive, making them difficult to be deployed on low power lightweight...
The sparse solver is a critical component in circuit simulators. The widely used solver KLU is based on a pure column-level algorithm. In this paper, we point out that KLU is not always the best algorithm for circuit matrices by experiments. We also demonstrate that the optimal algorithm strongly depends on the sparsity of the matrix. Two sparse LU factorization algorithms are proposed for extremely...
Memristor-based neuromorphic computing system provides a promising solution to significantly boost the power efficiency of computing system. Memristor-based neuromorphic computing system has a wide range of design choices, such as the various memristor crossbar cell designs and different parallelism degrees of peripheral circuits. However, a memristor-based neuromorphic computing system simulator,...
Deep Learning (DL) is becoming popular in a wide range of domains. Many emerging applications, ranging from image and speech recognition to natural language processing and information retrieval, rely heavily on deep learning techniques, especially the Neural Networks (NNs). NNs have led to great advances in recognition accuracy compared with other traditional methods in recent years. NN-based methods...
Convolutional Neural Network (CNN) is a powerful technique widely used in computer vision area, which also demands much more computations and memory resources than traditional solutions. The emerging metal-oxide resistive random-access memory (RRAM) and RRAM crossbar have shown great potential on neuromorphic applications with high energy efficiency. However, the interfaces between analog RRAM crossbars...
The crossbar array architecture with resistive synaptic devices is attractive for on-chip implementation of weighted sum and weight update in the neuro-inspired learning algorithms. This paper discusses the design challenges on scaling up the array size due to non-ideal device properties and array parasitics. Circuit-level mitigation strategies have been proposed to minimize the learning accuracy...
The spiking neural network (SNN) provides a promising solution to drastically promote the performance and efficiency of computing systems. Previous work of SNN mainly focus on increasing the scalability and level of realism in a neural simulation, while few of them support practical cognitive applications with acceptable performance. At the same time, based on the traditional CMOS technology, the...
PM2.5 has already been a major pollutant in many cities in China. It is a kind of harmful pollutant which may cause several kinds of lung diseases. However, the existing methods to monitor PM2.5 with high accuracy are too expensive to popularize. The high cost also limits the further researches about PM2.5. This paper implements a method to estimate PM2.5 with low cost and high accuracy by Artificial...
The invention of resistive-switching random access memory (RRAM) devices and RRAM crossbar-based computing system (RCS) demonstrate a promising solution for better performance and power efficiency. The interfaces between analog and digital units, especially AD/DAs, take up most of the area and power consumption of RCS and are always the bottleneck of mixed-signal computing systems. In this work, we...
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