Energy-aware DNN Quantization for Processing-In-Memory Architecture

With increasing computational cost of deep neural network (DNN), many efforts to develop energy-efficient intelligent system have been proposed from dedicated hardware platforms to model compression algorithms. Recently, hardware-aware quantization algorithms have shown further improvement in the energy efficiency of DNN by considering hardware architectures and algorithms together. In this work, a genetic algorithm-based energy-aware DNN quantization framework for Processing-In-Memory (PIM) architectures, named EGQ, is presented. The key contribution of the research is to design a fitness function that can reduce the number of analog-to-digital converter (ADC) access, which is one of the main energy overhead in PIM. EGQ automatically optimizes layer-wise weight and activation bitwidth with negligible accuracy loss while considering the dynamic energy in PIM. The research demonstrates the effectiveness of EGQ on several DNN models VGG-19, ResNet-18, ResNet-50, MobileNet-V2, and SqueezeNet. Also, the area, dynamic energy, and energy efficiency in the compressed models with various memory technologies are analyzed. EGQ shows 15%-103% higher energy efficiency with 2% accuracy loss than other PIM-aware quantization algorithms.M.S

Unsupervised Hebbian Learning on Point Sets in StarCraft II

Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to extract the global feature of point sets in StarCraft II game units, and its application to predict the movement of the points. Our model includes encoder, LSTM, and decoder, and we train the encoder with the unsupervised learning method. We introduce the concept of neuron activity aware learning combined with k-Winner-Takes-All. The optimal value of neuron activity is mathematically derived, and experiments support the effectiveness of the concept over the downstream task. Our Hebbian learning rule benefits the prediction with lower loss compared to self-supervised learning. Also, our model significantly saves the computational cost such as activations and FLOPs compared to a frame-based approach.Comment: Accepted in International Joint Conference on Neural Networks (IJCNN) 202

Analytical simulation of the effects of noise control treatments on an excavator cab using statistical energy analysis

The objective of this study was to utilize Statistical Energy Analysis (SEA) to simulate the effects of a variety of noise control treatments on the interior sound pressure level (SPL) of a commercial excavator cab. In addition, the effects of leaks on the SPL of the excavator cab were also investigated. This project was conducted along with various tests that were used to determine the inputs needed to accurately represent the loads that the cab experienced during operation. This paper explains the how the model was constructed, how the loads were applied to the model, the results that were obtained from application of treatments, and a study of the effects of introducing leaks to the cab structure in the SEA model

Application of signature analysis and operating deflection shapes to identify interior noise sources in an excavator

The objective of this study was to identify and gain an understanding of the origins of noise in a commercial excavator cab. This paper presents the results of two different tests that were used to characterize the vibration and acoustic characteristics of the excavator cab. The first test was done in an effort to characterize the vibration properties of the cab panels and their associated contribution to the noise level inside the cab. The second set, of tests, was designed to address the contribution of the external airborne noise produced by the engine and hydraulic pump to the overall interior noise. This paper describes the test procedures used to obtain the data for the signature analysis, operational deflection shapes (ODS), and sound diagnosis analysis. It also contains a discussion of the analysis results and an inside look into the possible contributors of key frequencies to the interior noise in the excavator cab

Immunogenic Effect of Hyperthermia on Enhancing Radiotherapeutic Efficacy

Hyperthermia is a cancer treatment where tumor tissue is heated to around 40 °C. Hyperthermia shows both cancer cell cytotoxicity and immune response stimulation via immune cell activation. Immunogenic responses encompass the innate and adaptive immune systems, involving the activation of macrophages, natural killer cells, dendritic cells, and T cells. Moreover, hyperthermia is commonly used in combination with different treatment modalities, such as radiotherapy and chemotherapy, for better clinical outcomes. In this review, we will focus on hyperthermia-induced immunogenic effects and molecular events to improve radiotherapy efficacy. The beneficial potential of integrating radiotherapy with hyperthermia is also discussed

Inhibitory effect of traditional oriental medicine-derived monoamine oxidase B inhibitor on radioresistance of non-small cell lung cancer

Increased survival of cancer cells mediated by high levels of ionizing radiation (IR) reduces the effectiveness of radiation therapy for non-small cell lung cancer (NSCLC). In the present study, danshensu which is a selected component of traditional oriental medicine (TOM) compound was found to reduce the radioresistance of NSCLC by inhibiting the nuclear factor-κB (NF-κB) pathway. Of the various TOM compounds reported to inhibit the IR activation of NF-κB, danshensu was chosen as a final candidate based on the results of structural comparisons with human metabolites and monoamine oxidase B (MAOB) was identified as the putative target enzyme. Danshensu decreased the activation of NF-κB by inhibiting MAOB activity in A549 and NCI-H1299 NSCLC cells. Moreover, it suppressed IR-induced epithelial-to-mesenchymal transition, expressions of NF-κB-regulated prosurvival and proinflammatory genes, and in vivo radioresistance of mouse xenograft models. Taken together, this study shows that danshensu significantly reduces MAOB activity and attenuates NF-κB signaling to elicit the radiosensitization of NSCLC

Decreased Hepatic Lactotransferrin Induces Hepatic Steatosis in Chronic Non-Alcoholic Fatty Liver Disease Model

Background/Aims: Non-alcoholic fatty liver disease (NAFLD) is an emerging metabolic disease. Although it leads to severe hepatic diseases including steatohepatitis, cirrhosis, and hepatic cancer, little is known about therapy to prevent and cure hepatic steatosis, the first step of NAFLD. We conducted this investigation to unveil the mechanism of hepatic steatosis. Methods: We established a novel chronic NAFLD mouse model through whole body irradiation and verified the model through histological and biochemical analysis. To find molecular mechanism for hepatic steatosis, we analyzed hepatic transcriptomic profiles in this model and selected target molecule. To induce the expression of lactotransferrin (Ltf) and regulate the NAFLD, growth hormone (GH) and coumestrol was introduced to hepatocyte and mice. The universal effect of coumestrol was confirmed by administration of coumestrol to NAFLD mouse model induced by high-fructose, high-fat, and MCD diet. Results: It was observed that decreased hepatic Ltf expression led to excessive hepatic lipid accumulation in NAFLD mouse. Furthermore, we found that GH was decreased in irradiated mice and functioned as an upstream regulator of Ltf expression. It was observed that GH could stimulate Ltf expression and prevent uptake of dietary lipids in hepatocytes, leading to rescue of NAFLD. Finally, we suggested that coumestrol, a kind of isoflavonoid, could be used as an inducer of hepatic Ltf expression through cooperation with the GH signaling pathway both in vitro and in vivo. Conclusions: Hepatic Ltf prevents hepatic steatosis through inhibition of dietary lipid uptake in radiation-induced NAFLD mouse model. We also suggest coumestrol as a drug candidate for prevention of NAFLD

High preoperative gait variability is a prognostic predictor of gait and balance in Parkinson disease patients with deep brain stimulation

© 2022Introduction: The objective biomarker for prediction of gait and balance in the long-term follow-up of Parkinson's disease(PD) patients with subthalamic nucleus deep brain stimulation(STN-DBS) has not yet been elucidated. We investigated the value of pre-operative quantitative gait parameters for the prediction of long-term prognosis of gait in PD patients with DBS. Methods: We retrospectively collected gait videos(both medication ON/OFF states) of PD patients recorded as preoperative evaluation before STN DBS. We enrolled patients who were followed-up for more than 5 years after the surgery from 2006 to 2014. We derived objective gait parameters from video-based gait analysis algorithm. We defined the clinical milestones of frequent falling, impaired walking, and loss of autonomy based on the Unified Parkinson's disease rating scale and Hoehn and Yahr stage, which were regularly followed up to 156 months after surgery. We calculated hazard ratios(HRs) of baseline gait parameters for predicting the clinical milestones. Results: A total of 96 gait videos from 63 PD patients were analyzed. The mean follow-up duration(standard deviation) was 88.0(34.2) months after surgery. Relatively high (>mean + 1 standard deviation) variability for step length, step time and stride time (HR = 2.92[1.02–8.33], 3.91[1.38–11.11] and 7.16[2.09–24.52],respectively) in medication-ON state significantly predicted reaching any of the three clinical milestones of frequent falling, impaired walking and loss of autonomy. Gait parameters from the medication-OFF state did not predict any clinical milestone. Conclusions: High preoperative gait variability from the medication-ON state predicts long-term outcomes for gait and balance in PD patients with STN DBS.N