DESIGN
EW: FPGAS
Artificial intelligence (AI) is transforming healthcare by
enabling advanced capabilities like real-time image analysis, continuous patient monitoring, and personalised diagnostics. FPGA technology has transformed the landscape of medical devices and diagnostics through its exceptional capabilities. By leveraging real-time processing, high performance computing, and customisable hardware architecture, FPGAs have revolutionised medical technology, enabling more precise diagnostics and enhanced patient care outcomes.
FPGAs powering AI in medical devices
D ue to the parallel processing Applications using medical imaging that require the speedy processing of large amounts of data can benefit from this capacity. The efficiency of medical picture reconstruction and analysis is improved by parallel processing, which also cuts down on overall processing time. FPGAs enable AI-driven analysis and allow for processing data directly at the Edge, providing personalised treatment recommendations based on real-time insights. This on-device processing reduces latency and also enhances privacy. capabilities of FPGAs, many tasks can be carried out concurrently. In the AI field, FPGAs are often used as AI accelerators and AI processors that help enable AI workloads from Edge to Cloud. The interconnectivity within an FPGA resembles the neural wiring in the human brain. The programmable logic fabric within
an FPGA is similarly connected, which is one reason why FPGAs are excellent implementation targets for neural networks and other AI workloads. Key applications of FPGAs in medical devices: • Medical imaging: ultra-sound/CT scanners/MRI machines • Patient monitoring: ECG and EEG monitors • Diagnostics and treatment: remote surgeries Case study: ultra-low latency streamer A ULL Streamer has been built by iWave around the Zynq UltraScale+ MPSoC FPGA. The ULL Streamer minimises this glass-to-glass delay to just 66ms over Wi-Fi
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