Under the "Impossible Triangle", how many hurdles do AI glasses chips still need to overcome?
This year, AI glasses have officially entered the large - scale production cycle, and the industry is growing strongly. According to IDC data, in the first quarter of 2026, the global smart glasses market grew at a year - on - year rate of up to 130.1%. The Chinese market ranked third globally with a 23.5% growth. It is estimated that the global shipments of smart glasses will reach 23.687 million units this year.
While the market heat continues to rise, the overall product experience in the industry generally has serious flaws. Problems such as obvious heating, short battery life, delays in visual recognition and real - time translation, and heavy devices have become the core pain points restricting user retention and industry advancement. From the underlying logic of the industry, the core problem lies in the immature dedicated chip system.
01
Three Major Flaws Drag Down the Terminal Experience
In the early stage of the development of the AI glasses industry, the industrial chain did not have a mature dedicated chip system. To quickly seize the market and lower the threshold for hardware R & D, many small and medium - sized device manufacturers directly used mid - end mobile phone SoCs for simple tailoring and adaptation. After only deleting some redundant modules such as basebands and high - definition video encoding, they directly embedded them into the narrow temples to complete the hardware adaptation.
However, the core design logic of mobile phone chips is centered around the large - screen body, large - capacity battery, and built - in heat dissipation space of smart phones. This is contrary to the special usage environment of AI glasses, which have a narrow body, passive heat dissipation, a 200 - 300mAh micro - battery, and are worn close to the body. It is difficult to achieve adaptation through simple software optimization or hardware fine - tuning, directly giving rise to three major terminal experience defects, which have become common pain points for mass - produced products in the industry.
First, there is obvious heating under high - load conditions, which destroys the wearing comfort. When a mobile phone SoC runs high - load tasks such as local large - model inference, real - time visual recognition, and continuous AR image rendering, the overall power consumption will instantly soar to several watts. The temple structure of all - in - one AI glasses is extremely simple, without any active heat dissipation structures such as fans or heat spreaders, and only relies on the plastic shell for passive heat conduction. The heat in the space cannot be dissipated quickly.
When the first - generation AI glasses equipped with a tailored mobile phone SoC run continuously under high load for 30 minutes, the hot spots in the temple area close to the skin generally exceed 48 - 52°C, far exceeding the comfortable and safe threshold of 39°C for wearable devices. Above 43°C, obvious burning discomfort is likely to occur. Even in daily intermittent use, its no - load standby power consumption is much higher than that of professional wearable chips.
Second, the running delay of AI core functions is too high. The core product competitiveness of AI glasses is concentrated in intelligent interaction functions such as real - time multilingual translation, first - person object recognition, gesture and eye - movement tracking, and spatial SLAM modeling. These functions have extremely high requirements for the chip's computing power response speed and require the NPU to have millisecond - level real - time inference ability exclusive to wearables.
However, the NPU architecture built into general mobile phone SoCs is mainly optimized for short - term computing power requirements such as static image processing on mobile phones and AI special effects in short videos, and is not adapted to the normal working scenarios of AI glasses, which involve continuous streaming image acquisition and parallel inference of voice and text. After frequency modulation under the strict power consumption and space limitations of the temples, the delay of local translation and visual recognition of the device will exceed 100 milliseconds. Image trailing, lagging translation subtitles, and freezing in object recognition have become common problems in mass - produced models, making the core intelligent functions of the product ineffective and unable to meet the user's real - time interaction needs.
Finally, the overall battery life of the device is short, unable to support all - day wearing requirements. The current mainstream all - in - one AI glasses with screens have a battery capacity of only 150mAh - 300mAh, with limited battery energy storage. The no - load standby power consumption and peak operating power consumption of mobile phone SoCs are both relatively high. In the normal scenario where voice wake - up, camera standby, and the display module are running constantly, the single - charge battery life of the device can only last for 2 - 4 hours. The market requires AI glasses to meet the intermittent use needs in all scenarios such as commuting, office work, travel, and sports. The short battery life of a few hours means that users need to carry a charging case with them to recharge frequently, which weakens the core product advantage of the portability of smart glasses.
If manufacturers try to improve the battery life by increasing the battery capacity, it will directly increase the overall weight of the device. Under the current general chip solution, the overall weight of most products exceeds 50g. Wearing them for a long time will cause obvious pressure on the nose bridge and ears. This is also a direct manifestation of the well - recognized "impossible triangle" of computing power, lightness, and battery life in the industry. To achieve a qualitative change in the product experience of AI glasses and promote the industry's upgrade from quantity to quality, the core technical thresholds of four types of dedicated supporting chips must be comprehensively broken through.
02
Each of the Five Core Chips Has Technical Barriers to Overcome
The AI glasses' overall system is a highly integrated micro - intelligent hardware, driven by the cooperation of the main control SoC, ISP image signal processor and CMOS image sensor, display driver chip, power management chip, and storage chip. Each of the five types of chips has its own functions, respectively determining the upper limit of the overall computing power, imaging effect, display experience, battery life, and body size of the device. However, each type of chip has different technical barriers that the industrial chain needs to overcome one by one.
As the computing core of the device, the main control SoC chip integrates core functions such as computing power processing, image imaging, wireless communication, and system management, directly determining the device's intelligent interaction ability, running delay, and overall power consumption performance. The problems it faces are also the most complex.
First, there is a core contradiction between computing power and power consumption. Local AI computing, high - definition image processing, and real - time rendering require high computing power support, but high computing power can easily lead to problems such as excessive power consumption, device heating, and shortened battery life. From the data, running a local multi - modal large model now requires at least 4 - 6 TOPS, and the computing power demand has increased tenfold. When running synchronously under high load, the chip's power consumption can reach around 300mW, and 60% of the battery power will be consumed in one hour of high - intensity use. At the same time, spatial interaction requires the image delay to be less than 20 milliseconds, while the delay of low - computing - power solutions generally exceeds 50ms. It is difficult to balance computing power, power consumption, size, and delay at the same time. This is also the most core technical pain point of the current SoC in the industry. Second, the device integration requirements are extremely high. The traditional discrete chip solution has a large volume and cannot be adapted to the lightweight design of the glasses. At the same time, concurrent operation of multiple tasks is prone to scheduling freezing and interaction delays, and the stability of voice recognition, image noise reduction, and wireless transmission in complex environments is insufficient, greatly affecting the user experience.
Currently, the mainstream solution is to launch SoCs with higher integration, which can provide stable computing power without occupying more space. Qualcomm's AR1 Gen1 series of SoCs are the benchmark solutions for high - end AR glasses, integrating a dedicated NPU, ISP image processor, and high - speed communication module, supporting end - side AI large - model computing and high - definition optical engine rendering. They have mature computing power scheduling and strong stability, and are widely used in Meta Ray - Ban and high - end AR wearable devices, which can balance the needs of high - performance computing and low - delay interaction. Domestic SoC manufacturers are rapidly iterating and making breakthroughs, focusing on high computing power energy efficiency ratio and high cost - performance. Rockchip, Ankai Micro, CoolChip Micro, BES, UNISOC, Allwinner and other manufacturers have specifically launched dedicated wearable SoC chips, optimizing image processing and lightweight AI computing power, and adapting to basic functions such as daily shooting, voice interaction, and real - time screen mirroring.
The ISP image signal processor and CMOS image sensor are the core partners of the AI glasses' visual perception system, responsible for real - scene collection, image quality optimization, image noise reduction, and AI visual input, directly determining the accuracy and effect of the device's photo imaging, spatial recognition, and environmental perception.
Similarly, limited by the insufficient space of the glasses body, large - size sensors cannot be installed, resulting in insufficient light intake. In low - light environments, the images have many noise points and poor clarity. At the same time, the wearing posture of the device is changeable and mobile shooting is frequent, which is prone to problems such as motion trailing and image shaking.
The high - end AI glasses image supply chain has long been dominated by international leading manufacturers, with high technical barriers. Sony and Samsung have the core capabilities of high photosensitivity, high dynamic range, and ultra - low noise in low - light environments due to their high - quality CMOS sensor process advantages. They have mature pixel adjustment and accurate color restoration, and their products are natively adapted to wide - angle wearable lenses, suppressing the power consumption of a single camera within 120mW while achieving high - speed imaging, meeting the needs of high - end AR glasses for spatial positioning and high - definition shooting. In the ISP field, Qualcomm, ADI and other manufacturers' dedicated image processing chips support multi - level noise reduction, HDR dynamic brightening, and motion compensation algorithms, which can quickly correct the image distortion of wearable devices during shooting and greatly improve the imaging stability in dynamic scenarios.
Among domestic manufacturers, OmniVision, SmartSens, and GalaxyCore have specifically launched dedicated wearable miniaturized CMOS image sensors, optimizing the photosensitive performance while reducing the chip volume, and adapting to the lightweight module design of glasses.
The display driver chip is the core component of AI glasses with screens and is also the chip category with the most prominent supply shortages and technical contradictions in the current supply chain. It directly determines the screen clarity, color performance, image delay, and overall power consumption of the device, and is the key to improving the experience of AR perspective, real - time screen mirroring, and AI text - image display.
Currently, AI glasses are generally equipped with Micro - OLED, LCoS, and Micro LED micro - displays. Limited by the lightweight design of the device, the display driver chip faces multiple industry pain points. On the one hand, micro - displays have high pixel density and extremely small sizes. Traditional driver chips are prone to problems such as image trailing, uneven color, and low - brightness stroboscopic, and the display clarity is insufficient in outdoor strong - light scenarios. On the other hand, high - refresh - rate and high - color - gamut driving will significantly increase power consumption, easily causing the overall battery life of the device to shrink, making it difficult to balance the needs of high - definition display and low power consumption. At the same time, the narrow space of the glasses body has strict requirements for chip miniaturization and low EMI interference. When multiple modules work together, display delays and image shaking are prone to occur, affecting the wearing visual experience.
Solomon Systech, Realtek and other Taiwanese manufacturers have deep technical accumulations. Their driver chips have high color accuracy, low delay, and adaptive brightness adjustment capabilities, can be adapted to mainstream optical engines such as LCoS and Micro - OLED, effectively solve the problems of stroboscopic and color distortion, and are widely used in high - end AR glasses models such as Meta and Huawei. Visionox Technology has been deeply involved in the field of micro - display drivers, optimizing the architecture according to the lightweight and low - power consumption requirements of AI glasses, adapting to various types of micro - displays, and quickly penetrating the domestic market with high - cost - performance solutions.
As the central control unit for overall power consumption scheduling, the power management chip coordinates the charging and discharging of the device and the power supply regulation of the entire link. The temples of AI glasses cannot be equipped with large - capacity batteries, and the battery is the heaviest part of the entire product. Most AI glasses use a split - power - supply structure with dual batteries in the left and right temples to balance the weight. However, this also easily leads to problems such as uneven charging and discharging of the dual batteries and inconsistent voltage differences, reducing battery utilization and overall battery life.
ADI's solution is to launch an integrated PMIC chip, which has ultra - low static power consumption, high - precision battery capacity measurement, and a single - inductor multi - output architecture, can streamline peripheral components, and is adapted to the miniaturization design requirements of flagship models such as Meta Ray - Ban, effectively alleviating the problem of limited space. TI, NXP, Qorvo's power management series chips support multi - rail voltage regulation, dynamic load adaptation, and hierarchical sleep control, can match the dynamic computing power fluctuations of end - side AI chips, and balance performance and power consumption. Qualcomm's dedicated PMIC is deeply adapted to its AR wearable platform, precisely coordinating the overall power supply timing, and is the core power supply solution for high - performance computing AI glasses. Southchip Technology's dual - battery balancing chip specifically solves the industry pain point of uneven charging and discharging of dual - temple batteries, effectively improving the overall battery life. SGMICRO's low - noise voltage regulators and multi - channel power supply chips can ensure the stable operation of precision components such as cameras and 3D perception sensors. AWINIC, HDSemi and other manufacturers have completed the supporting links such as peripheral power supply and auxiliary voltage regulation. The entire power supply solution is widely used in mainstream AI glasses such as Xiaomi, TCL, and Alibaba Quark.
Storage chips are also crucial in AI glasses, shouldering the important task of storing and retrieving data. As the functions of AI glasses become increasingly powerful and diverse, AI glasses need to process and store more data, such as high - definition videos, high - resolution images, and continuously upgraded AI models. The capacity of storage chips will continue to increase, developing from the currently common 32GB to higher capacities to ensure that the device can run various complex applications smoothly. Moreover, they account for a relatively high proportion of the hardware cost of AI glasses. Taking Meta's Ray - Ban smart glasses as an example, Biwin Storage's storage chips (ROM + RAM) account for about 7% of its BOM cost, second only to the main control SoC chip, with a single - unit value of about $11.
ePOP and eMCP are the current mainstream storage integration solutions for AI glasses in the market. ePOP, or embedded stacked packaging technology, achieves a high degree of integration by vertically stacking NAND Flash and LPDDR above the SoC. The biggest advantage of this technology is that it can save about 60% of the PCB space, making the internal layout of AI glasses more compact and contributing to the lightweight design of the device. At the same time, by reducing the number of chips and connection lines, power consumption is reduced, thereby improving the device's battery life. With its features of small size, low power consumption, and high performance, this technology solution has been applied by companies such as Meta, Google, and Facebook in AI smart glasses, smart watches, and other products.
eMCP, or embedded multi - chip packaging technology, uses multi - chip packaging technology to integrate eMMC and LPDDR in one package. It has a built - in NAND Flash control chip, which can reduce the computing burden of the main chip, simplify the management of large - capacity flash memory, and also save motherboard space. In some AI glasses that have certain requirements for storage capacity and performance but need to control costs, eMCP technology has been widely used. It can provide a relatively economical storage solution for the device while ensuring a certain level of performance, meeting the market demand for mid - to - low - end AI glasses.
In addition to the above - mentioned chip types, AI glasses also include audio processing chips, communication chips, Bluetooth chips, etc. In the future, if AI glasses are to truly become the most popular consumer electronic products besides mobile phones, all these core chips need to be upgraded simultaneously; none of them can be missing.
03
External Problems Are Also Tricky
The AI glasses chip track also faces two major external problems: unstable supply chain and divergence of industry architecture routes, which have become key obstacles to the industry's development.
At the supply chain level, the explosive growth of the AI server industry has continuously occupied the core production capacity of wafer foundries and storage chips. The production capacity of low - power LPDDR and small - capacity Flash storage chips originally adapted to wearable devices has been greatly compressed. The original manufacturers' production capacity is tilted, and the spot prices continue to rise, resulting in an increase in the inventory cost of AI glasses device manufacturers. For AI glasses, the operation of local large models, real - time image caching, and storage of shooting materials all rely on dedicated low - power storage chips. The occupation of production capacity has left many small and medium - sized device manufacturers facing difficulties in obtaining goods and high costs. Some niche brands have even postponed the launch of new products due to the supply interruption of storage chips, seriously restricting the overall shipment growth rate of the industry.
At the same time, the problem of insufficient production capacity of micro - display driver chips has become more prominent. The LCoS