IR-QNN Framework: An IR Drop-Aware Offline Training Of Quantized Crossbar Arrays

Resistive Crossbar Arrays present an elegant implementation solution for Deep Neural Networks acceleration. The Matrix-Vector Multiplication, which is the corner-stone of DNNs, is carried out in O(1) compared to O(N-2) steps for digital realizations of O(log(2)(N)) steps for in-memory associative processors. However, the IR drop problem, caused by the inevitable interconnect wire resistance in RCAs remains a daunting challenge. In this article, we propose a fast and efficient training and validation framework to incorporate the wire resistance in Quantized DNNs, without the need for computationally extensive SPICE simulations during the training process. A fabricated four-bit Au/Al2O3/HfO2/TiN device is modelled and used within the framework with two-mapping schemes to realize the quantized weights. Efficient system-level IR-drop estimation methods are used to accelerate training. SPICE validation results show the effectiveness of the proposed method to capture the IR drop problem achieving the baseline accuracy with a 2% and 4% drop in the worst-case scenario for MNIST dataset on multilayer perceptron network and CIFAR 10 dataset on modified VGG and AlexNet networks, respectively. Other nonidealities, such as stuck-at fault defects, variability, and aging, are studied. Finally, the design considerations of the neuronal and the driver circuits are discussed

Simultaneous Multislice Brain MRI T1 Mapping with Improved Low-Rank Modeling

To accelerate data acquisition speed in magnetic resonance imaging (MRI), multiple slices are simultaneously acquired using multiband pulses. Simultaneous multislice (SMS) imaging typically unfolds slice aliasing from the acquired collapsed slices. In this study, we extended the SMS framework to accelerated MR parameter quantification such as T1 mapping. Assuming that the slice-specific null space and signal subspace are invariant along the parameter dimension, we formulated the SMS framework as a constrained optimization problem under a joint reconstruction framework such that the noise and signal subspaces are used for slice separation and recovery, respectively. The proposed method was validated on 3T MR human brain scans. We successfully demonstrated that the proposed method outperforms competing methods in suppressing aliasing artifacts and noise at high SMS accelerations, thus leading to accurate T1 maps

Simultaneous Multislice Brain MRI T1 Mapping with Improved Low-Rank Modeling

To accelerate data acquisition speed in magnetic resonance imaging (MRI), multiple slices are simultaneously acquired using multiband pulses. Simultaneous multislice (SMS) imaging typically unfolds slice aliasing from the acquired collapsed slices. In this study, we extended the SMS framework to accelerated MR parameter quantification such as T1 mapping. Assuming that the slice-specific null space and signal subspace are invariant along the parameter dimension, we formulated the SMS framework as a constrained optimization problem under a joint reconstruction framework such that the noise and signal subspaces are used for slice separation and recovery, respectively. The proposed method was validated on 3T MR human brain scans. We successfully demonstrated that the proposed method outperforms competing methods in suppressing aliasing artifacts and noise at high SMS accelerations, thus leading to accurate T1 maps

On-chip memory optimization for high-level synthesis of multi-dimensional data on FPGA

It is very challenging to design an on-chip memory architecture for high-performance kernels with large amount of computation and data. The on-chip memory architecture must support efficient data access from both the computation part and the external memory part, which often have very different expectations about how data should be accessed and stored. Previous work provides only a limited set of optimizations. In this paper we show how to fundamentally restructure on-chip buffers, by decoupling logical array view from the physical buffer view, and providing general mapping schemes for the two. Our framework considers the entire data flow from the external memory to the computation part in order to minimize resource usage without creating performance bottleneck. Our experimental results demonstrate that our proposed technique can generate solutions that reduce memory usage significantly (2X over the conventional method), and successfully generate optimized on-chip buffer architectures without costly design iterations for highly optimized computation kernels

Double MAC on a DSP: Boosting the Performance of Convolutional Neural Networks on FPGAs

Deep learning such as Convolutional Neural Networks (CNNs) are an important workload increasingly demanding high-performance hardware acceleration. One distinguishing feature of deep learnng workload is that it is inherently resilient to small numerical errors and works very well with low precision hardware. Thus we propose a novel method, called Double MAC, to theoretically double the computation rate of CNN accelerators by packing two multiply-and-accumulate (MAC) operations into one DSP block of off-the-shelf FPGAs. There are several technical challenges, which we overcome by exploiting the mode of operation in the CNN accelerator. We have validated our method through FPGA synthesis and Verilog simulation, and evaluated our method by applying it to the state-of-the-art CNN accelerator. We find that our Double MAC approach can increase the computation throughput of a CNN layer by twice. On the network level (all convolution layers combined), the performance improvement varies depending on the CNN application and FPGA size, from 14% to more than 80% over a highly optimized state-of-the-art accelerator solution, without sacrificing the output quality significantly

Optimization of Gate-All-Around Device to Achieve High Performance and Low Power with Low Substrate Leakage

In this study on multi-nanosheet field-effect transistor (mNS-FET)—one of the gate-all-around FETs (GAAFET) in the 3 nm technology node dimension—3D TCAD (technology computer-aided design) was used to attain optimally reduced substrate leakage from options including a punch-through-stopper (PTS) doping scheme and a bottom oxide (BO) scheme for bottom isolation, with the performance improvement being shown in the circuit-level dynamic operation using the mNS-FET. The PTS doping concentration requires a high value of >5 × 1018 cm−3 to reduce gate induced drain leakage (GIDL), regardless of the presence or absence of the bottom isolation layer. When the bottom isolation is applied together with the PTS doping scheme, the capacitance reduction is larger than the on-state current reduction, as compared to when only the PTS doping concentration is applied. The effects of such transistor characteristics on the performance and capabilities of various circuit types—such as an inverter ring oscillator (RO), a full adder (FA) circuit, and a static random-access memory (SRAM)—were assessed. For the RO, applying BO along with the PTS doping allows the operating speed to be increased by 11.3% at the same power, or alternatively enables 26.4% less power consumption at the same speed. For the FA, power can be reduced by 6.45%, energy delay product (EDP) by 21.4%, and delay by 16.8% at the same standby power when BO and PTS are both applied. Finally, for the SRAM, read current (IREAD) increased by 18.7% and bit-line write margin (BWRM) increased by 12.5% at the same standby power. Through the circuit simulations, the Case 5 model (PTS doping concentration: 5.1 × 1018 cm−3, with BO) is the optimum condition for the best device and circuit performance. These observations confirm that PTS and bottom isolation applications in mNS-FETs can be utilized to enable the superior characteristics of such transistors to translate into high performance integrated circuits

Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation

The purpose of this research was to derive promising technologies for the transport of hydrogen fuel cells, thereby supporting the development of research and development policy and presenting directions for investment. We also provide researchers with information about technology that will lead the technology field in the future. Hydrogen energy, as the core of carbon neutral and green energy, is a major issue in changing the future industrial structure and national competitive advantage. In this study, we derived promising technology at the core of future hydrogen fuel cell transportation using the published US patent and paper databases (DB). We first performed text mining and data preprocessing and then discovered promising technologies through generative topographic mapping analysis. We analyzed both the patent DB and treatise DB in parallel and compared the results. As a result, two promising technologies were derived from the patent DB analysis, and five were derived from the paper DB analysis

Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation

The purpose of this research was to derive promising technologies for the transport of hydrogen fuel cells, thereby supporting the development of research and development policy and presenting directions for investment. We also provide researchers with information about technology that will lead the technology field in the future. Hydrogen energy, as the core of carbon neutral and green energy, is a major issue in changing the future industrial structure and national competitive advantage. In this study, we derived promising technology at the core of future hydrogen fuel cell transportation using the published US patent and paper databases (DB). We first performed text mining and data preprocessing and then discovered promising technologies through generative topographic mapping analysis. We analyzed both the patent DB and treatise DB in parallel and compared the results. As a result, two promising technologies were derived from the patent DB analysis, and five were derived from the paper DB analysis

Promising Technology Analysis and Patent Roadmap Development in the Hydrogen Supply Chain

Hydrogen energy, one of the energy sources of the future, represents a substantial issue which affects the industries and national technologies that will develop in the future. In order to utilize hydrogen energy, a hydrogen supply chain is required so that hydrogen can be processed and transported to vehicles. It is helpful for technology and policy development to analyze technologies necessary to charge the hydrogen energy generated into vehicles through the supply chain to discover technologies with high potential for future development. The purpose of this paper is to identify promising technologies required in storing, transporting, and charging vehicles generated by the hydrogen fuel supply chain. Afterward, the promising technologies identified are expected to help researchers set a direction in researching technologies and developing related policies. Therefore, we provide technology information that can be used promisingly in the future so that researchers in the related field can utilize it effectively. In this paper, data analysis is performed using related patents and research papers for technical analysis. Promising technologies that will be the core of the hydrogen fuel supply chain in the future were identified using the published patents and research paper database (DB) in Korea, the United States, Europe, China, and Japan. A text mining technique was applied to preprocess data, and then a generic topographic map (GTM) analysis discovered promising technologies. Then, a technology roadmap was identified by analyzing the promising technology derived from patents and research papers in parallel. In this study, through the analysis of patents and research papers related to the hydrogen supply chain, the development status of hydrogen storage/transport/charging technology was analyzed, and promising technologies with high potential for future development were found. The technology roadmap derived from the analysis can help researchers in the field of hydrogen research establish policies and research technologies

Promising Technology Analysis and Patent Roadmap Development in the Hydrogen Supply Chain

Hydrogen energy, one of the energy sources of the future, represents a substantial issue which affects the industries and national technologies that will develop in the future. In order to utilize hydrogen energy, a hydrogen supply chain is required so that hydrogen can be processed and transported to vehicles. It is helpful for technology and policy development to analyze technologies necessary to charge the hydrogen energy generated into vehicles through the supply chain to discover technologies with high potential for future development. The purpose of this paper is to identify promising technologies required in storing, transporting, and charging vehicles generated by the hydrogen fuel supply chain. Afterward, the promising technologies identified are expected to help researchers set a direction in researching technologies and developing related policies. Therefore, we provide technology information that can be used promisingly in the future so that researchers in the related field can utilize it effectively. In this paper, data analysis is performed using related patents and research papers for technical analysis. Promising technologies that will be the core of the hydrogen fuel supply chain in the future were identified using the published patents and research paper database (DB) in Korea, the United States, Europe, China, and Japan. A text mining technique was applied to preprocess data, and then a generic topographic map (GTM) analysis discovered promising technologies. Then, a technology roadmap was identified by analyzing the promising technology derived from patents and research papers in parallel. In this study, through the analysis of patents and research papers related to the hydrogen supply chain, the development status of hydrogen storage/transport/charging technology was analyzed, and promising technologies with high potential for future development were found. The technology roadmap derived from the analysis can help researchers in the field of hydrogen research establish policies and research technologies