# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Energy-Efficient Task Scheduling
#### 5.3.1 Event-Driven Approach  
Designs based on an event-driven approach involve activating hardware components only in response to specific events rather than running them continuously. This can significantly reduce energy consumption by ensuring that components are only activated when necessary, thus avoiding wasteful energy expenditure during idle periods. The authors of [72] proposed a two-stage ECG classifier using an event-driven approach. The idea behind this is the observation that most heartbeats are usually normal. The algorithm is implemented on a Zynq XC7Z020 FPGA, where the first stage only differentiates between normal and abnormal classes. The Softmax function normalizes the output of the first stage. If the classification score exceeds some preset value, then the classifier sends the normal class as the output; otherwise, the second stage will be triggered to further classify the abnormal class into different types of arrhythmia. Another approach implemented a three-level event-driven mechanism for arrhythmia classification on a Xilinx XC5 VXL10T [115]. The ECG signal, converted through LC-ADC, is segmented into independent heartbeats by the QRS locator module. The correlation information among heartbeats is then reproduced by the beat segment reactor module. This reconstructed information is then further classified using an abnormal heartbeat detector module. Upon detection of an abnormal heartbeat, the cardiac arrhythmia classifier is awakened to classify the heartbeat further; otherwise, it remains dormant. The authors reported a 44% reduction in power consumption when operating in detection mode compared with classification mode. A neuromorphic processor was proposed on an event-driven based architecture, where a spiking neural network (SNN) is used with a leaky integrate-and-fire (LIF) model of neurons for ECG classification [116]. At every time stamp, the potential is integrated by an input spike and is also lowered by a leaky term. When the integrated value of potential reaches a threshold, a firing event happens. In the proposed processor, an event handler is designed for each of the three phases of the LIF model. The event controller triggers an event handler based on the timestamp and ID of the neuron received in the processor via the bus. These event handlers are triggered based on the conclusion of the previous event. The processor remains in an idle state for the rest of the time. They were able to reduce power consumption by 21.69% and resources by \(\approx\)15% at the cost of reduced accuracy of 83%. The authors of [117] also used an event-driven concept in epileptic seizure detection, dividing the process into monitoring and detection stages. The monitoring stage extracts only one feature to avoid redundant computations and operates in both inter-ictal and ictal states. The detection stage remains powered off and is only activated temporarily by the controller when a detection event happens in the monitoring stage. It extracts more features to improve accuracy and thus reduce false positive alarms in the first stage. They were able to reduce dynamic power consumption to 1 uW with a trade-off between detection speed and false positive detection. A similar approach was also used in [114], where upon detecting a low battery event, the transmission mode was switched from transmitting complete raw data to sending only critical data to conserve power. In case an anomalous event is detected in ECG data, the system can revert back to transmitting the complete raw signal for detailed analysis at the receiving end. This significantly reduces the transmission load and power consumption. In [20], the authors also implemented an event-driven signal reconstruction module. Their system is divided into three modules: compression, analysis, and reconstruction. The reconstruction module remains dormant and only activates when an anomaly is detected during the analysis of compressed data, thereby saving power consumption.