Abstract-High Level Synthesis (HLS) languages and tools are emerging as the most promising technique to make FPGAs more accessible to software developers. Nevertheless, picking the most suitable HLS for a certain class of algorithms depends on requirements such as area and throughput, as well as on programmer experience.In this paper, we explore the different trade-offs present when using a representative set of HLS tools in the context of Database Management Systems (DBMS) acceleration. More specifically, we conduct an empirical analysis of four representative frameworks (Bluespec SystemVerilog, Altera OpenCL, LegUp and Chisel) that we utilize to accelerate commonly-used database algorithms such as sorting, the median operator, and hash joins. Through our implementation experience and empirical results for database acceleration, we conclude that the selection of the most suitable HLS depends on a set of orthogonal characteristics, which we highlight for each HLS framework.

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Voice quality assessment is required by healthcare professionals in patients suffering from voice problems. Speech and language therapists (SLTs) use a well-known subjective assessment approach which is called GRBAS, to quantify voice problems. GRBAS is an acronym for a five dimensional scale of measurements of voice properties which were originally recommended by the Japanese Society of Logopeadics and Phoniatrics and the European Research for clinical and research use. The properties are `Grade', `Roughness', `Breathiness', `Asthenia' and `Strain'. In requiring the services of trained SLTs, this subjective assessment make the GRBAS measurement expensive to administer. In this research, computerised objective measurement of `Strain' in voice using two regression prediction models is compared with measurements produced by SLTs according to the GRBAS scale. These regression models are K Nearest Neighbor Regression (KNNR) and Multiple Linear Regression (MLR). These new approaches for prediction of Strain are based on different subsets of features, different sets of data, and different prediction models in comparison with previous approaches in the literature. The best feature subset for predicting Strain objectively was obtained amongst different feature subsets. When compared with the mean of five SLT's scores, over 102 samples, the computerised measurement was found to have a Normalized Root Mean Square Error (NRMSE) averaged over 20 trials, lower than that of each individual SLT. We have achieved a NRMSE of 14.6% and 15.1% for the MLR and KNNR respectively when the best feature subsets were used for predicting Strain objectively.

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