According to reports from Electronic Enthusiasts (by Huang Shanming), the smart toy market has grown rapidly in recent years, with features such as voice recognition and image interaction becoming key competitive elements. However, high chip costs and development difficulties have made it challenging for many developers to advance their projects. How can we find suitable solutions that balance chip performance, power consumption, and cost? This article summarizes the core principles and development experiences of chip selection based on practical cases.
The Three Core Principles of AI Toy Chip Selection
For AI toys, higher chip computing power is not always better; it needs to match specific scenarios. For example, a toy that can recognize 10 voice commands may only require a local computing power of 0.5 TOPS, which is far below the image processing requirements of a smartphone. Therefore, developers should prioritize the chip’s scenario adaptability, energy efficiency, and development friendliness.
Taking the ESP32-C3 from Espressif Systems as an example, this chip costs only $1-2 and integrates Wi-Fi 6 and Bluetooth 5.0 dual-mode connectivity, supporting the TensorFlow Lite Micro framework, and can run lightweight gesture recognition models. Its RISC-V architecture not only reduces costs but also lowers the development threshold through a rich Arduino ecosystem.
In contrast, while the Nordic nRF52840 excels in Bluetooth connection stability, its $3 price and limited computing power make it more suitable for voice interaction devices that require long battery life.
However, the rise of domestic chips has provided new options for low-cost solutions. For instance, the GD32E230 series offers BLE 5.0 support for less than $2 and is compatible with the mature Arduino development environment, making it particularly suitable for small and medium developers in the local supply chain. Meanwhile, the Kendryte K210, with its built-in KPU neural network accelerator, can achieve facial detection and other visual functions for under $5, and has been widely used in programming education robots.
The Synergy Between Local and Cloud Computing
In ultra-low-cost scenarios, the implementation of AI functions requires flexible use of local computing and cloud collaboration. For resource-constrained chips, relying entirely on local processing often means compromising performance, while excessive dependence on the cloud may be limited by network stability. Therefore, a hybrid architecture has become the mainstream choice.
For example, in a toy that supports voice interaction, its core functions can be divided into three levels. The first is local preprocessing, where the chip’s ADC collects microphone signals and uses a TinyLSTM model to detect keywords like “start” and “stop,” avoiding the upload of invalid audio to the cloud; secondly, edge inference runs a lightweight voice recognition model (such as a simplified version of MobileNetV2) on the chip to provide real-time feedback for simple commands; finally, for more complex tasks (like semantic understanding or multi-turn dialogue), the server processes them in the cloud, transmitting data via the MQTT protocol.
This layered strategy leverages the real-time response advantages of local chips while utilizing cloud computing power for complex tasks. For instance, a toy equipped with the ESP32-C3 can filter out 90% of meaningless commands through local keyword detection, sending only valid requests to the cloud, resulting in an overall power consumption reduction of over 40%.
Hidden Costs and Development Traps in Development
Many developers tend to overlook the hidden costs of chips: for example, a chip may have high nominal computing power, but its sleep power consumption could be as high as 10mA, leading to a battery life of less than 3 hours; or a certain solution may require an external RF module to achieve Wi-Fi connectivity, increasing the BOM cost by $2. Therefore, energy efficiency metrics and the complexity of peripheral circuits must be considered during the selection phase.
Taking the nRF52840 as an example, its sleep power consumption is as low as 0.1μA, and when combined with an accelerometer interrupt wake-up mechanism, it allows motion-sensing toys to have almost zero power consumption in standby mode. Meanwhile, the domestic GD32E230 series eliminates the need for an external RF chip by integrating the BLE 5.0 protocol stack, further reducing hardware costs.
In low-cost AI toy development, many projects fail due to overlooked details. For instance, a team added image classification functionality to a toy but did not perform model quantization, resulting in the 512KB Flash storage being filled; in another case, developers chose a chip without hardware encryption, leading to user voice data being intercepted during transmission.
Therefore, the INT8 quantization tool from TensorFlow Lite Micro can be used to reduce the model size to one-fourth of the original. Additionally, it is advisable to prioritize chips that support TLS/DTLS protocols (such as the ESP32-S3) to prevent data leakage. For scenarios that continuously run the KPU accelerator (like facial detection), heat sinks should be added or task durations limited to avoid chip overheating and frequency reduction.
Conclusion
In the field of AI toys, the essence of chip selection reflects systematic thinking—it requires developers to move beyond a single parameter comparison and weigh multiple dimensions such as scenario requirements, development costs, and supply chain risks. With the popularization of RISC-V architecture and the maturity of Chiplet technology, modular chip solutions may even emerge in the future: developers could freely combine NPU, RF modules, and other units to build the most suitable AI hardware like assembling building blocks. For small and medium teams, this may mean lower trial and error costs and greater innovation space.
Disclaimer: This article is original from Electronic Enthusiasts, please indicate the source above when reprinting. If you wish to join the group for discussion, please add WeChat elecfans999, for submission of interview requests, please send an email to [email protected].
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