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Deep learning research has generated widespread interest leading to emergence of a large variety of technological innovations and applications. As significant proportion of deep learning research focuses on vision based applications, there exists a potential for using some of these techniques to enable lowpower portable health-care diagnostic support solutions. In this paper, we propose an embedded-hardware-based implementation of microscopy diagnostic support system for PoC case study on: (a) Malaria in thick blood smears, (b) Tuberculosis in sputum samples, and (c) Intestinal parasite infection in stool samples. We use a Squeeze-Net based model to reduce the network size and computation time. We also utilize the Trained Quantization technique to further reduce memory footprint of the learned models. This enables microscopy-based detection of pathogens that classifies with laboratory expert level accuracy as a standalone embedded hardware platform. The proposed implementation is 6x more power-efficient compared to conventional CPU-based implementation and has an inference time of ∼ 3 ms/sample.

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In this paper, we present first-ever optimized hardware implementation of a state-of-the-art neuromorphic approach Histogram of Averaged Time Surfaces (HATS) algorithm to event-based object classification in FPGA for asynchronous time-based image sensors (ATIS). Our Implementation achieves latency of 3.3 ms for the N-CARS dataset samples and is capable of processing 2.94 Mevts/s. Speed-up is achieved by using parallelism in the design and multiple Processing Elements can be added. As development platform, Zynq-7000 SoC from Xilinx is used. The tradeoff between Average Absolute Error and Resource Utilization for fixed precision implementation is analyzed and presented. The proposed FPGA implementation is ∼ 32 x power efficient compared to software implementation.