Axp: A hw-sw co-design pipeline for energy-efficient approximated convnets via associative matching

The reduction in energy consumption is key for deep neural networks (DNNs) to ensure usability and reliability, whether they are deployed on low-power end-nodes with limited resources or high-performance platforms that serve large pools of users. Leveraging the over-parametrization shown by many DNN models, convolutional neural networks (ConvNets) in particular, energy efficiency can be improved substantially preserving the model accuracy. The solution proposed in this work exploits the intrinsic redundancy of ConvNets to maximize the reuse of partial arithmetic results during the inference stages. Specifically, the weight-set of a given ConvNet is discretized through a clustering procedure such that the largest possible number of inner multiplications fall into predefined bins; this allows an off-line computation of the most frequent results, which in turn can be stored locally and retrieved when needed during the forward pass. Such a reuse mechanism leads to remarkable energy savings with the aid of a custom processing element (PE) that integrates an associative memory with a standard floating-point unit (FPU). Moreover, the adoption of an approximate associative rule based on a partial bit-match increases the hit rate over the pre-computed results, maximizing the energy reduction even further. Results collected on a set of ConvNets trained for computer vision and speech processing tasks reveal that the proposed associative-based hw-sw co-design achieves up to 77% in energy savings with less than 1% in accuracy loss

Deep Adaptive Temporal Pooling for Activity Recognition

Deep neural networks have recently achieved competitive accuracy for human activity recognition. However, there is room for improvement, especially in modeling of long-term temporal importance and determining the activity relevance of different temporal segments in a video. To address this problem, we propose a learnable and differentiable module: Deep Adaptive Temporal Pooling (DATP). DATP applies a self-attention mechanism to adaptively pool the classification scores of different video segments. Specifically, using frame-level features, DATP regresses importance of different temporal segments, and generates weights for them. Remarkably, DATP is trained using only the video-level label. There is no need of additional supervision except video-level activity class label. We conduct extensive experiments to investigate various input features and different weight models. Experimental results show that DATP can learn to assign large weights to key video segments. More importantly, DATP can improve training of frame-level feature extractor. This is because relevant temporal segments are assigned large weights during back-propagation. Overall, we achieve state-of-the-art performance on UCF101, HMDB51 and Kinetics datasets

PREDICTING RATINGS FOR USER REVIEWS AND OPINION MINING ANALYZE FOR PHYSICIANS AND HOSPITALS

ABSTRACTHealth care is taking its turn in the internet now and online health information consumption is also booming. Users have started generating healthcarereports like online doctor reviews open to all. Hence, online health forums are increasingly popular these days since people can gather their requireddata by just sitting at home and select the best doctor by considering the reviews available online. The patients also browse on their concerneddiseases and use the open forum for discussion on the topics. On an average, these online health-care providers are mainly focusing on reviews aboutthe physicians. The feedback provided by patients is considered and we also analyze the sentiments of the patient to estimate the value of the reviews.The rating for the doctors is divided into various categories such as Staff, Knowledge, and Helpfulness. We propose support vector machine and apriorifor the classification of data and use sentiment based rating prediction to analyze doctor's reviews and opinion mining patterns for online patterns.By providing physician ratings in website, it offers the patients to know about the physician and consider the critique and information to make theirdecision.Keywords: Support vector machine, Apriori, Sentiment classification, Opinion mining