FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks

In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent neural networks (RNNs); one is a speech-to-character RNN for acoustic modeling (AM) and the other is for character-level language modeling (LM). The system also employs a statistical word-level LM to improve the recognition accuracy. The results of the AM, the character-level LM, and the word-level LM are combined using a fairly simple N-best search algorithm instead of the hidden Markov model (HMM) based network. The RNNs are implemented using massively parallel processing elements (PEs) for low latency and high throughput. The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA. The proposed algorithm is implemented on a Xilinx XC7Z045, and the system can operate much faster than real-time.Comment: Accepted to SiPS 201

Self-assembled nanocomplex between polymerized phenylboronic acid and doxorubicin for efficient tumor-targeted chemotherapy

Since the discovery that nano-scaled particulates can easily be incorporated into tumors via the enhanced permeability and retention (EPR) effect, such nanostructures have been exploited as therapeutic small molecule delivery systems. However, the convoluted synthetic process of conventional nanostructures has impeded their feasibility and reproducibility in clinical applications. Herein, we report an easily prepared formulation of self-assembled nanostructures for systemic delivery of the anti-cancer drug doxorubicin (DOX). Phenylboronic acid (PBA) was grafted onto the polymeric backbone of poly(maleic anhydride). pPBA-DOX nanocomplexes were prepared by simple mixing, on the basis of the strong interaction between the 1,3-diol of DOX and the PBA moiety on pPBA. Three nanocomplexes (1, 2, 4) were designed on the basis of [PBA]:[DOX] molar ratios of 1: 1, 2: 1, and 4: 1, respectively, to investigate the function of the residual PBA moiety as a targeting ligand. An acid-labile drug release profile was observed, owing to the intrinsic properties of the phenylboronic ester. Moreover, the tumor-targeting ability of the nanocomplexes was demonstrated, both in vitro by confocal microscopy and in vivo by fluorescence imaging, to be driven by an inherent property of the residual PBA. Ligand competition assays with free PBA pre-treatment demonstrated the targeting effect of the residual PBA from the nanocomplexes 2 and 4. Finally, the nanocomplexes 2 and 4, compared with the free DOX, exhibited significantly greater anti-cancer effects in vitro and even in vivo. Our pPBA-DOX nanocomplex enables a new paradigm for self-assembled nanostructures with potential biomedical applications.115Ysciescopu

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets

We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes

A New Surgical Approach to Treat the Resistant Hypertension

The sympathetic nervous system was known to play an important role in resistant hypertension. Surgical sympathectomy for renal sympathetic nerve removal were performed since the 1930s. Although effective, it had many serious side effects and complications due to severe invasiveness and non-selective property. Recently, catheter based renal denervation (RDN) system using radiofrequency (RF) ablation was developed to overcome invasiveness of sympathectomy. While RF ablation technology was considered promising, it failed to show effectiveness in a recent sham controlled trial. Therefore, there is a strong need for safe and effective RDN strategies considering the anatomical structure of the renal arteries and sympathetic nerves. In this paper, we propose a novel surgical instrument for laparoscopic RDN and show its feasibility through a 3D realistic model using nephrectomy tissues. Laparoscopic RDN will become a new surgical approach to effectively and safely remove renal sympathetic nerves.2

Exploration of On-device End-to-End Acoustic Modeling with Neural Networks

Real-time speech recognition on mobile and embedded devices is an important application of neural networks. Acoustic modeling is the fundamental part of speech recognition and is usually implemented with long short-term memory (LSTM)-based recurrent neural networks (RNNs). However, the single thread execution of an LSTM RNN is extremely slow in most embedded devices because the algorithm needs to fetch a large number of parameters from the DRAM for computing each output sample. We explore a few acoustic modeling algorithms that can be executed very efficiently on embedded devices. These algorithms reduce the overhead of memory accesses using multitime-step parallelization that computes multiple output samples at a time by reading the parameters only once from the DRAM. The algorithms considered are the quasi RNNs (QRNNs), Gated ConvNets, and diagonalized LSTMs. In addition, we explore neural networks that equip one-dimensional (1-D) convolution at each layer of these algorithms, and by which can obtain a very large performance increase in QRNNs and Gated ConvNets. The experiments were conducted using the connectionist temporal classification (CTC)-based end-to-end speech recognition on WSJ corpus. We not only significantly increase the execution speed but also obtain a much higher accuracy, compared to LSTM RNN-based modeling. Thus, this work can be applicable not only to embedded system-based implementations but also to server-based ones.N