Recurrent neural networks (RNN) are extensively used to determine the optimal solutions to the various class recognition problems such as image processing, prediction of biomedical data and speech recognition. With the gradient problems, RNN is slowing losing its shade which is replaced by the Long short term memory (LSTM). However the hardware implementation of the LSTM requires more challenge due to its complexity and high power consumption which makes it unsuitable for implementing in Biological Internet of things networks for prediction of heart diseases. Several algorithms were proposed for an effective implementation of LSTM, but hand-offs between the performance and utilization still needs improvisation. The paper proposes the novel energy efficient and high performance architecture Pipelined Stochastic Adaptive Distributed Architectures (P-SCADA) for LSTM networks. In this architecture, hybrid structure has been developed with the help of new distributed arithmetic stochastic computing (DSC) along with the binary circuits to advance the performance of the FPGA such as energy, area and accuracy. The proposed system has been implemented in ARTIX-7 FPGA with special purpose software has been designed and evaluated with different ECG datasets. For the different series data, area utilization is about 40%–44% and power consumption is about 20%–25% with the prediction of accuracy of 98%. Moreover the proposed architecture has been compared with the other existing architecture such as SPARSE architectures, normal stochastic architectures in which the proposed architecture excels in terms area, power and efficiency.

中文翻译：

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P-SCADA - 一种新型面积和节能 FPGA 架构，用于 LSTM 预测生物应用中的心律失常

循环神经网络 (RNN) 广泛用于确定各种类别识别问题的最佳解决方案，例如图像处理、生物医学数据的预测和语音识别。由于梯度问题，RNN 正在减慢失去阴影的速度，被长短期记忆 (LSTM) 取代。然而，LSTM 的硬件实现由于其复杂性和高功耗而需要更多的挑战，这使得它不适合在生物物联网网络中实现以预测心脏病。为有效实现 LSTM 提出了几种算法，但性能和利用率之间的切换仍然需要即兴发挥。本文提出了用于 LSTM 网络的新型节能和高性能架构流水线随机自适应分布式架构 (P-SCADA)。在该架构中，借助新的分布式算术随机计算 (DSC) 以及二进制电路开发了混合结构，以提高 FPGA 的性能，例如能量、面积和精度。所提出的系统已在 ARTIX-7 FPGA 中实现，并使用不同的 ECG 数据集设计和评估了专用软件。对于不同系列数据，面积利用率约为 40%~44%，功耗约为 20%~25%，预测准确率为 98%。此外，所提出的架构已与其他现有架构（例如 SPARSE 架构）进行了比较，