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In the era of the Internet of Things (IoT), wearable IoT devices become popular and closely related to our life. Most of these devices are based on the embedded systems that have to operate on limited energy resources, such as batteries or energy harvesters. Therefore, energy efficiency is one of the critical issues for these devices. To relieve the energy consumption by reducing the total accesses on memory and storage layers, the technologies of storage-class memory (SCM) and data compression techniques are applied to eliminate the data movements and squeeze the data size, respectively. However, the information gap between them hinders the cooperation among the two techniques for achieving further optimizations on minimizing energy consumption. This work proposes a write-friendly arithmetic coding with joint managing both techniques to achieve energy-efficient non-volatile memory (NVM) systems. In particular, the concept of "ignorable bits" is introduced to further skip the write operations while storing the compressed data into SCM devices. The proposed design was evaluated by a series of intensive experiments, and the results are encouraging.

Proton radiation therapy is one of the most effective modern methods of cancer treatment. Because radiation cannot distinguish between tumors and healthy tissue, simulating beams to predict the range of radiation is crucial. Monte Carlo simulation is the most accurate dose calculation method for radiotherapy. However, high accuracy requires extremely long computation time, thus limiting its clinical applications. To date, considerable efforts have been made to utilize graphics processing unit (GPU)-accelerated algorithm designs for accelerating the proton dose simulation. However, to completely utilize the capability of a specific GPU, the GPU configurations must be fine-tuned carefully. In this study, we propose the performance evaluation of GPU-accelerated Monte Carlo simulation programs for proton therapy. Considerable efforts have been made to properly use an autotuning tool to determine optimal configurations for the aforementioned programs. Furthermore, the source of counterintuitive observations was examined. Evaluation results show that appropriate configurations can considerably reduce the simulation time of programs.