Implementation of bioinspired algorithms on the neuromorphic VLSI system SpiNNaker 2

It is believed that neuromorphic hardware will accelerate neuroscience research and enable the next generation edge AI. On the other hand, brain-inspired algorithms are supposed to work efficiently on neuromorphic hardware. But both processes don't happen automatically. To efficiently bring together hardware and algorithm, optimizations are necessary based on the understanding of both sides. In this work, software frameworks and optimizations for efficient implementation of neural network-based algorithms on SpiNNaker 2 are proposed, resulting in optimized power consumption, memory footprint and computation time. In particular, first, a software framework including power management strategies is proposed to apply dynamic voltage and frequency scaling (DVFS) to the simulation of spiking neural networks, which is also the first-ever software framework running a neural network on SpiNNaker 2. The result shows the power consumption is reduced by 60.7% in the synfire chain benchmark. Second, numerical optimizations and data structure optimizations lead to an efficient implementation of reward-based synaptic sampling, which is one of the most complex plasticity algorithms ever implemented on neuromorphic hardware. The results show a reduction of computation time by a factor of 2 and energy consumption by 62%. Third, software optimizations are proposed which effectively exploit the efficiency of the multiply-accumulate array and the flexibility of the ARM core, which results in, when compared with Loihi, 3 times faster inference speed and 5 times lower energy consumption in a keyword spotting benchmark, and faster inference speed and lower energy consumption for adaptive control benchmark in high dimensional cases. The results of this work demonstrate the potential of SpiNNaker 2, explore its range of applications and also provide feedback for the design of the next generation neuromorphic hardware

Dynamic Power Management for Neuromorphic Many-Core Systems

This work presents a dynamic power management architecture for neuromorphic many core systems such as SpiNNaker. A fast dynamic voltage and frequency scaling (DVFS) technique is presented which allows the processing elements (PE) to change their supply voltage and clock frequency individually and autonomously within less than 100 ns. This is employed by the neuromorphic simulation software flow, which defines the performance level (PL) of the PE based on the actual workload within each simulation cycle. A test chip in 28 nm SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct PLs. By measurement of three neuromorphic benchmarks it is shown that the total PE power consumption can be reduced by 75%, with 80% baseline power reduction and a 50% reduction of energy per neuron and synapse computation, all while maintaining temporary peak system performance to achieve biological real-time operation of the system. A numerical model of this power management model is derived which allows DVFS architecture exploration for neuromorphics. The proposed technique is to be used for the second generation SpiNNaker neuromorphic many core system

Knowledge structure and hotspots research of glioma immunotherapy: a bibliometric analysis

BackgroundGlioma is the most common primary brain tumor. Traditional treatments for glioma include surgical resection, radiotherapy, chemotherapy, and bevacizumab therapy, but their efficacies are limited. Immunotherapy provides a new direction for glioma treatment. This study aimed to summarize the knowledge structure and research hotspots of glioma immunotherapy through a bibliometric analysis.MethodPublications pertaining to glioma immunotherapy published during the period from 1st January 1990 to 27th March 2023 were downloaded from the Web of Science Core Collection (WoSCC). Bibliometric analysis and visualization were performed using the CiteSpace, VOSviewer, Online Analysis Platform of Literature Metrology, and R software. The hotspots and prospects of glioma immunotherapy research were illustrated via analyzing the countries, institutions, journals, authors, citations and keywords of eligible publications.ResultsA total of 1,929 publications pertaining to glioma immunotherapy in 502 journals were identified as of 27th March 2023, involving 9,505 authors from 1,988 institutions in 62 countries. Among them were 1,285 articles and 644 reviews. Most of publications were produced by the United States. JOURNAL OF NEURO-ONCOLOGY published the majority of publications pertaining to glioma immunotherapy. Among the authors, Lim M contributed the largest number of publications. Through analyzing keyword bursts and co-cited references, immune-checkpoint inhibitors (ICIs) were identified as the research focus and hotspot.ConclusionUsing a bibliometric analysis, this study provided the knowledge structure and research hotspots in glioma immunotherapy research during the past 33 years, with ICIs staying in the current and future hotspot. Our findings may direct the research of glioma immunotherapy in the future

Prediction of ESRD in IgA Nephropathy Patients from an Asian Cohort: A Random Forest Model

Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model

Highly efficient cash sterilization with ultrafast and flexible Joule‐heating strategy by laser patterning

Since ancient times, humans have learned to use fire and other heating methods to fight against dangerous pathogens, like cooking raw food, sterilizing surgical tools, and disinfecting other pathogen transmission media. However, it remains difficult for current heating methods to achieve extremely fast and highly efficient sterilization simultaneously. Herein, an ultrafast and uniform heating‐based strategy with outstanding bactericidal performance is proposed. Ultra‐precise laser manufacturing is used to fabricate the Joule heater which can be rapidly heated to 90 °C in 5 s with less than 1 °C fluctuation in a large area by real‐time temperature feedback control. An over 98% bactericidal efficiency on S. aureus for 30 s and on E. coli for merely 5 s is shown. The heating strategy shows a 360 times faster acceleration compared to the commonly used steam sterilization from the suggested guidelines by the Centers for Disease Control and Prevention (CDC), indicating that high temperatures with short duration can effectively disinfect microorganisms. As a proof of concept, this heating strategy can be widely applied to sterilizing cash and various objects to help protect the public from bacteria in daily life

Implementation of bioinspired algorithms on the neuromorphic VLSI system SpiNNaker 2

It is believed that neuromorphic hardware will accelerate neuroscience research and enable the next generation edge AI. On the other hand, brain-inspired algorithms are supposed to work efficiently on neuromorphic hardware. But both processes don't happen automatically. To efficiently bring together hardware and algorithm, optimizations are necessary based on the understanding of both sides. In this work, software frameworks and optimizations for efficient implementation of neural network-based algorithms on SpiNNaker 2 are proposed, resulting in optimized power consumption, memory footprint and computation time. In particular, first, a software framework including power management strategies is proposed to apply dynamic voltage and frequency scaling (DVFS) to the simulation of spiking neural networks, which is also the first-ever software framework running a neural network on SpiNNaker 2. The result shows the power consumption is reduced by 60.7% in the synfire chain benchmark. Second, numerical optimizations and data structure optimizations lead to an efficient implementation of reward-based synaptic sampling, which is one of the most complex plasticity algorithms ever implemented on neuromorphic hardware. The results show a reduction of computation time by a factor of 2 and energy consumption by 62%. Third, software optimizations are proposed which effectively exploit the efficiency of the multiply-accumulate array and the flexibility of the ARM core, which results in, when compared with Loihi, 3 times faster inference speed and 5 times lower energy consumption in a keyword spotting benchmark, and faster inference speed and lower energy consumption for adaptive control benchmark in high dimensional cases. The results of this work demonstrate the potential of SpiNNaker 2, explore its range of applications and also provide feedback for the design of the next generation neuromorphic hardware

Implementation of bioinspired algorithms on the neuromorphic VLSI system SpiNNaker 2

It is believed that neuromorphic hardware will accelerate neuroscience research and enable the next generation edge AI. On the other hand, brain-inspired algorithms are supposed to work efficiently on neuromorphic hardware. But both processes don't happen automatically. To efficiently bring together hardware and algorithm, optimizations are necessary based on the understanding of both sides. In this work, software frameworks and optimizations for efficient implementation of neural network-based algorithms on SpiNNaker 2 are proposed, resulting in optimized power consumption, memory footprint and computation time. In particular, first, a software framework including power management strategies is proposed to apply dynamic voltage and frequency scaling (DVFS) to the simulation of spiking neural networks, which is also the first-ever software framework running a neural network on SpiNNaker 2. The result shows the power consumption is reduced by 60.7% in the synfire chain benchmark. Second, numerical optimizations and data structure optimizations lead to an efficient implementation of reward-based synaptic sampling, which is one of the most complex plasticity algorithms ever implemented on neuromorphic hardware. The results show a reduction of computation time by a factor of 2 and energy consumption by 62%. Third, software optimizations are proposed which effectively exploit the efficiency of the multiply-accumulate array and the flexibility of the ARM core, which results in, when compared with Loihi, 3 times faster inference speed and 5 times lower energy consumption in a keyword spotting benchmark, and faster inference speed and lower energy consumption for adaptive control benchmark in high dimensional cases. The results of this work demonstrate the potential of SpiNNaker 2, explore its range of applications and also provide feedback for the design of the next generation neuromorphic hardware

Chinese Web Comments Clustering Analysis with a Two-phase Method

Usually a meaningful web topic has tens of thousands of comments, especially the hot topics. It is valuable if we congregate the comments into clusters and find out the mainstreams. However, such analysis has two difficulties. First, there is no explicit link relationship between web comments just like those among web pages or Blog comments. The other problem is, most of the comments are very short, even one or two words. Therefore the traditional clustering algorithms such as CURE and DBSCAN cannot work if applied to these comments directly. In this paper we propose a two-phase algorithm, which will first combine the highly synonymous comments into a longer one based on a connected graph model, and then apply the improved clustering methods to the new collections. Experimental results on two real data sets show that our algorithm performs better than traditional algorithms such as CURE. ? 2009 IEEE.EI