A “compact and efficient” approach to edge AI requires end-to-end design, from chip selection to power management. Computing and memory resources must be matched to specific tasks, with each module active only when needed and promptly entering sleep mode when idle. This is how AI capabilities translate into faster response, longer battery life and a better user experience.
Demand for AI servers is reshaping supply and demand across the memory industry. According to a TrendForce survey published in July 2026, DRAM supply remained tight in the third quarter, while NAND Flash demand continued to be supported by AI inference and data center expansion, adding to cost pressures in the consumer market. The impact varies by component, but device makers must all assess configuration costs more carefully against functional benefits.
Through its work with smart hardware customers, Fortune Techgroup has observed growing interest in bringing practical AI capabilities into products while working within limited cost and battery budgets. For headphones, smart glasses, home appliances and IoT devices, product design must answer two questions: Which chips are needed to perform the required tasks? And how will those chips work together and consume power in real-world use?
Chip Selection Should Start with Tasks and Usage Patterns
Voice wake-up, sound event detection, image classification and sensor data analysis can often be handled by purpose-built, optimized models. Before selecting chips, define which functions must run continuously and which are triggered only when needed, as well as the requirements for response latency, recognition accuracy and offline operation.
Consider AI headphones: low-power wake-up, clear voice capture and complex question answering place different demands on computing resources. A low-power path can handle continuous detection; a DSP can process audio; an NPU can accelerate supported neural-network inference; and a CPU can manage system control. Requests requiring general knowledge can be routed to a phone or the cloud, depending on the product. The specific division of work depends on the chip architecture, operator support and software toolchain.
Main processor selection should therefore consider task efficiency, on-chip memory, sleep and wake-up capabilities, peripheral interfaces and wireless connectivity. To determine whether a dedicated chip is worthwhile, account for its own power consumption, communication overhead and impact on the main processor’s active time. Higher peak computing performance or fewer chips alone do not necessarily mean lower overall system power.
Memory Configuration Affects Both Cost and Power
Flash typically stores firmware, models and data, while SRAM, PSRAM or DRAM holds runtime data, intermediate results and buffers. Configuration must account for model weights, peak memory use during inference, audio/video buffers, the operating system and space for updates—not just the model file size.
Quantization, pruning and distillation can reduce a model’s memory and compute requirements, but their impact on recognition quality and real-world performance must be validated on the target hardware. Without compromising functionality, reducing unnecessary memory use, external accesses and data transfers can help control system overhead. The results depend on the memory type, access patterns and support for low-power states.
Memory should not be reduced indiscriminately. An undersized buffer may cause more frequent processor wake-ups, data processing or wireless transmissions. A well-balanced configuration allows models, buffers and compute units to work together while retaining sufficient headroom for future upgrades.
Power Management Is About the Entire System’s Operating States
A chip’s minimum sleep current describes only one specific operating state. Overall battery life also depends on the power consumption and duration of sensing, inference, communication and playback, as well as the overhead of switching between states. The power budget should cover the full usage cycle, including power-conversion losses and the quiescent consumption of peripheral circuits.
For voice-enabled devices that must remain responsive, a low-power microphone and detection path can listen continuously, waking the main processor or launching a high-load task only when a valid event is detected. A suitable pre-roll buffer can preserve the beginning of an event. Once the task is complete, software should release resources promptly and place the processor, memory and peripherals into appropriate low-power states.
The effectiveness of this approach depends on the actual trigger rate. Too many false wake-ups repeatedly activate high-power modules, while an overly high detection threshold may miss valid events. Optimization should consider recognition performance, the number of wake-ups and the energy consumed per task to find the right balance for each use case.
Power-supply design must also be coordinated with system operation. Clock and power controls should reflect each module’s operating state; regulator efficiency and quiescent current under light loads should be considered; and power stability must be verified under transient loads such as wireless transmission. For battery-powered devices, testing must cover both continuous low-current consumption and short bursts of high power.
Low-power performance must ultimately be demonstrated through whole-device testing. Measure average power, peak current and response time under standby, normal interaction, frequent triggering and weak-network conditions, then estimate battery life based on actual usage patterns.
Edge–Cloud Collaboration Must Account for Communication Energy
Offloading complex tasks to the cloud can reduce a device’s computing and memory requirements, but wireless connectivity, data uploads, waiting and retransmissions also consume energy. The division of work between device and cloud should account for task complexity, upload volume, network conditions and latency requirements.
One practical approach for voice-enabled devices is to handle wake-up, essential audio preprocessing and simple device control locally, while uploading more complex requests only when needed. For data that can tolerate delays, buffering and batched transmission may be worthwhile. Real-time interactions, however, should prioritize responsiveness rather than compromise the user experience to reduce transmission frequency.
Wireless settings also affect overall system power. For Bluetooth Low Energy connections, connection interval, transmit power and data rate should be tuned to the actual link conditions. Weak signals can cause packet loss and retransmissions, extending the radio’s active time. Systems should define retry and retransmission policies while retaining basic functionality when disconnected. Audio and video uploads must also be subject to user consent and data protection measures.
Fortune Techgroup Connects Chip Selection with Whole-System Power Optimization
Fortune Techgroup has long focused on wireless connectivity, audio/video interaction, sensors and AIoT applications, while continuing to expand into edge AI solutions as a solution provider. Its understanding of both upstream chip platforms and downstream application scenarios enables the company to work with customers to balance functionality, cost and power consumption.
During product definition, Fortune Techgroup helps establish compute, memory and power budgets around typical usage flows and define which tasks run on-device or in the cloud. During solution development, it integrates the main processor, audio/video, wireless connectivity and sensors across hardware and software, incorporating wake-up, task scheduling and sleep mechanisms into the system design. During validation, whole-device integration and scenario-based testing assess battery life, response speed, connection stability and production consistency.
Fortune Techgroup aims to help customers move beyond selecting the right chips to making effective use of every unit of compute, every byte of memory and every bit of battery power—delivering AI device solutions that are cost-effective, reliable in use and ready for mass production.
This is also what the shift from “AI-enabled devices” to “AI-native devices” requires: sensing, computing, interaction and power management must be designed around AI tasks from the earliest stages of product definition. Rising memory costs make resource efficiency increasingly important. The lasting value of a “compact and efficient” approach lies in making limited hardware resources deliver useful, reliable intelligence over time.
Previous chapter： FROM RECORDER TO AI ASSISTANT: DESIGN PRACTICE FOR CELLULAR-CONNECTED AI PIN TERMINALS
