Low-Power Design Strategies for Camera Modules

Here are some strategies for low-power design in cameras:

Image Sensor: Choose sensors with low-power modes. For instance, some CMOS image sensors can enter an ultra-low power sleep mode when idle, only waking up when an image needs to be captured. This can significantly reduce power consumption. Moreover, new backside illuminated (BSI) sensors can offer lower power consumption than traditional front-illuminated sensors at the same performance level, as they utilize light more efficiently and reduce the power needed to achieve sufficient brightness.

Processor ( SoC): Use low-power system-on-chip (SoC) processors. These chips are often made with advanced manufacturing processes, such as TSMC's low-power process, which can reduce both static and dynamic power consumption. Additionally, the power management unit within the SoC can dynamically adjust the voltage and frequency of various modules based on the workload, avoiding unnecessary energy use.

Other Peripheral Devices: Choose low-power models for peripheral devices like Wi-Fi and Bluetooth modules. For example, Bluetooth Low Energy (BLE) modules can enter sleep mode when data transmission is infrequent, reducing power consumption.

Power Management Circuit Design: Design efficient power management circuits to reduce energy loss through proper power distribution and conversion. For example, use switch-mode power supplies instead of linear power supplies, as they are more efficient and can convert the input voltage more effectively to the required operating voltages for the camera's components. Also, add multiple power switches in the circuit to control the power supply to various components based on different operating modes (like idle, preview, and recording), enabling fine-grained power management.

Reduce Circuit Parasitic Parameters: During the PCB design phase, optimize routing and component placement to reduce parasitic capacitance and inductance in the circuit. These parasitic parameters can cause energy loss during signal transmission, so reducing them can improve circuit efficiency and lower power consumption. For example, shorten the length of high-frequency signal lines to reduce signal reflection and attenuation, thereby lowering the power consumption during signal transmission.

Smart Sleep and Wake Mechanism: The software controls the to enter sleep mode when it's not needed (e.g., no motion detected or no operations for a long time). In sleep mode, unnecessary hardware components, as the video encoder and Wi-Fi transmission module, are turned off, leaving only a low-power monitoring module (like a motion sensor) to detect if the camera to be awakened. When the monitoring module detects wake conditions (like motion trigger or remote control command), it quickly wakes up the camera and restores its working state.

Frame Rate Adjustment: Dynamically adjust the video frame rate based on the scene's dynamics and user needs. For example, in a surveillance scene, if the image unchanged for a long time, the frame rate can be reduced to decrease data processing and transmission, thereby lowering power consumption. Increase the frame rate again when there's or when detailed observation is needed.

Lower Resolution: In scenes where high image detail isn't required, lower the image resolution through software settings. Lower resolution means less for the image sensor to collect and lower workload for the video encoder, thus reducing power consumption. For instance, in remote surveillance where only a general view of the is needed, lower resolution can be used for preview.

Image and Video Processing Algorithm Optimization: Optimize the camera's internal image and video algorithms to reduce computation. For example, in image compression algorithms, use more efficient coding methods like H.265/HEVC. Compared to traditional H264 coding, these can reduce data volume while maintaining the same image quality, lowering the video encoder's power consumption. Also, optimize image enhancement and filtering to reduce unnecessary computation steps and improve algorithm efficiency.

Smart Detection Algorithm Optimization: For target detection and facial recognition algorithms in smart cameras, optimize the neural network structure or lightweight models to reduce computation while maintaining detection accuracy. For example, using depthwise separable convolutions instead of traditional convolutions can significantly reduce computation, thereby lowering power consumption of the processor running these algorithms.