Reinforcement learning (RL) based dynamic voltage and frequency scaling (DVFS) is an effective approach to balance performance and power consumption for video rendering applications. To approximate the “God’s eye view” regulation, the CPU-GPU should be fine-grained regulated and the SoC have to be fully observed by a RL-based DVFS governor. Fine-grained regulation with traditional value-based RL governor suffers action space explosion and it is impossible to have a fully observable SoC. To address these two issues, a governor based on deep recurrent deterministic gradient (DRDPG) governor is proposed. The governor is based on Deterministic Policy Gradient (DDPG) algorithm with embedded recurrent neural network (RNN). The DDPG algorithm guarantees fine-grained power regulation without action space explosion and the RNN-FC network topology mitigates the partial observability issue. Evaluated on the Nvidia Jetson NX platform, the proposed DRDPG governor achieves over 19% better regulation efficiency compared with Linux default governors and shows superior regulation efficiency to other RL-based state-of-the-arts. Implemented in a 55-nm CMOS process, the proposed governor draws merely 2.16-mA from a 1.2-V supply at 1-MHz clock occupying a silicon area of 0.075mm$^2$.

Recently, deep learning has been proven to be a promising approach in standard dynamic range (SDR) image compression. However, due to the wide luminance distribution of high dynamic range (HDR) images and the lack of large standard datasets, developing a deep model for HDR image compression is much more challenging. To tackle this issue, we view HDR data as distributional shifts of SDR data and the HDR image compression can be modeled as an out-of-distribution generalization (OoD) problem. Herein, we propose a novel out-of-distribution (OoD) HDR image compression framework (OoDHDR-codec). It learns the general representation across HDR and SDR environments, and allows the model to be trained effectively using a large set of SDR datases supplemented with much fewer HDR samples. Specifically, OoDHDR-codec consists of two branches to process the data from two environments. The SDR branch is a standard blackbox network. For the HDR branch, we develop a hybrid system that models luminance masking and tone mapping with white-box modules and performs content compression with black-box neural networks. To improve the generalization from SDR training data on HDR data, we introduce an invariance regularization term to learn the common representation for both SDR and HDR compression. Extensive experimental results show that the OoDHDR codec achieves strong competitive in-distribution performance and state-of-the-art OoD performance. To the best of our knowledge, our proposed approach is the first work to model HDR compression as OoD generalization problems and our OoD generalization algorithmic framework can be applied to any deep compression model in addition to the network architectural choice demonstrated in the paper. Code available at https://github.com/caolinfeng/OoDHDR-codec.

To deploy deep learning algorithms on resource-limited scenarios, an emerging device-resistive random access memory (ReRAM) has been regarded as promising via analog computing. However, the practicability of ReRAM is primarily limited due to the weight drifting of ReRAM neural networks due to multi-factor reasons, including manufacturing, thermal noises, and etc. In this paper, we propose a novel Bayesian optimization method for fault tolerant neural network architecture (BayesFT). For neural architecture search space design, instead of conducting neural architecture search on the whole feasible neural architecture search space, we first systematically explore the weight drifting tolerance of different neural network components, such as dropout, normalization, number of layers, and activation functions in which dropout is found to be able to improve the neural network robustness to weight drifting. Based on our analysis, we propose an efficient search space by only searching for dropout rates for each layer. Then, we use Bayesian optimization to search for the optimal neural architecture robust to weight drifting. Empirical experiments demonstrate that our algorithmic framework has outperformed the state-of-the-art methods by up to 10 times on various tasks, such as image classification and object detection.

The aim of clinical decision support implementing electronic health records is to satisfy the physicians' information needs. We are motivated to propose a self-attention based network on query expansion. Considering the difficulty and cost of medical text annotation and inspired by the idea of migration learning, we choose the Semantic Textual Similarity dataset for model training. Different from the previous work, the proposed approach is not only considering the score of a single term as an expansion term, but also taking the score of term combination into account. Our model utilizes Convolutional Neural Networks (CNN) to obtain sentence representation and self-attention mechanism for entity representation. With self-attention, it is able to estimate the weight of each entity to learn better representation for all entities. We conduct the experiments on three standard datasets of Text REtrieval Conference Clinical Decision Support Track, where the approach has a promising overall performance over the strong baselines.