According to a report by Electronic Enthusiasts (by Li Wanwan), in the current wave of digitalization sweeping the globe, edge artificial intelligence is reshaping the industrial landscape at an unprecedented speed. Market research data indicates that the global AI chip market is expected to exceed $120 billion by 2025, with an average annual compound growth rate of over 25%, and edge computing chips growing at a rate of 35%. This growth momentum is driven by the urgent demand for low-power, low-latency, and high-privacy computing in scenarios such as wearables, smart security, and smart industry.However, alongside the rapid development of the industry, traditional von Neumann architecture faces technical challenges such as the “memory wall” bottleneck, high power consumption, and imbalanced energy efficiency, which are becoming key constraints on the large-scale implementation of edge AI. In this context, Electronic Enthusiasts specially interviewed Pine Chip Technology, a company specializing in integrated storage and computing chips, to explore its innovative path to overcoming these technical challenges.
Pine Chip Technology: Integrated Storage and Computing Architecture Reshapes Edge Computing Paradigms
Pine Chip Technology was established in 2021 and is a chip design company focused on integrated storage and computing technology. It aims to accelerate artificial intelligence computing using emerging integrated storage and computing technology, with the goal of achieving high-performance AI computing engines at low costs, leading the construction of a non-von Neumann computing system and ecosystem. Its core team consists of scientists from Tsinghua University and technical experts from semiconductor giants such as Qualcomm and Micron, with deep expertise in memory design, neural network acceleration, and low-power architecture.“The traditional von Neumann architecture faces severe bottlenecks of ‘memory wall’ and ‘power wall’, where frequent data migration between memory and computing units leads to 60% to 80% energy waste, severely limiting the real-time performance of edge AI chips,” said Yang Yue, co-founder and CEO of Pine Chip Technology. “Our integrated storage and computing architecture fundamentally breaks through this limitation by directly performing multiply-accumulate operations within SRAM memory, achieving deep integration of computing and storage.”Pine Chip Technology has adopted several key technological innovations to build competitive barriers: firstly, a hybrid precision storage and computing array design that achieves high parallelism in matrix-vector multiplication through the collaborative optimization of pure digital computation and digital domain control, reaching an energy efficiency ratio of 27 TOPS/W at a 28nm process. Secondly, an adaptive sparse computing engine that utilizes the sparsity characteristics of neural networks to dynamically identify through hardware accelerators, achieving a performance improvement of 2-5 times. Thirdly, a multi-level power management technology that integrates clock gating, voltage islands, and dynamic voltage frequency adjustment, reducing standby power consumption to the μW level while ensuring computing performance.“Additionally, at the system architecture level, we have also achieved significant breakthroughs in key indicators for our integrated storage and computing chips through data flow optimization technology, operator fusion technology, and zero-copy data processing mechanisms, such as achieving a core computing energy efficiency ratio of 27 TOPS/W and reducing inference latency to the millisecond level,” Yang Yue introduced. “These technological advantages give our solutions a unique competitive edge in many edge AI application scenarios that are sensitive to real-time performance and power consumption, providing customers with better cost performance and stronger product differentiation capabilities.”Pine Chip Technology has developed several products, including PIMCHIP-S300 and PIMCHIP-N300. The PIMCHIP-S300 is a multi-modal intelligent perception decision-making AI chip equipped with an SRAM-based integrated storage and computing acceleration unit, featuring AI computing power integration, multi-modal fusion perception, cross-domain intelligent decision-making, ultra-low power consumption, and rapid response. It can provide end-to-end solutions and applications for voice, image, radar, etc., enhancing computing efficiency and reducing energy consumption in fields such as smart wearable devices, smart security, embodied intelligence, AI large models, and health data analysis.The PIMCHIP-N300 is a next-generation integrated storage and computing neural network processing unit designed for machine learning and artificial intelligence, capable of efficiently processing algorithms and models of artificial neural networks. The team also provides a free software toolchain that is highly flexible and easy to integrate, shortening product development cycles and accelerating customers’ brand intelligence upgrades.Currently, Pine Chip Technology’s chips are mainly applied in industries such as smart wearable devices, smart security, smart industry, smart healthcare, and educational intelligence.When discussing the unique advantages of its products, Yang Yue believes they are mainly reflected in three aspects: firstly, the leading energy efficiency ratio advantage. By achieving ‘zero data movement’ through the integrated storage and computing architecture, it reaches an energy efficiency ratio of 27 TOPS/W at a 28nm process, which is an order of magnitude improvement compared to traditional architectures. This technological breakthrough allows Pine Chip Technology’s chips to consume only 1/10 of the power of competitors at the same computing power, while achieving over 10 times the computing power at the same power consumption.Secondly, rapid iteration and verification capabilities. Based on modular IP core design and mature SRAM processes, Pine Chip Technology can quickly complete the full iteration from algorithm optimization to chip verification, shortening the development cycle by over 60% compared to the traditional 12-18 months.Thirdly, deep optimization for edge scenarios. In response to the ‘low power consumption, small size, and high real-time’ demands of edge AI applications, Pine Chip Technology has developed dedicated neural network acceleration engines and adaptive power management technologies, achieving millisecond-level response while ensuring computing accuracy.“Our customer manufacturers faced severe challenges when developing the next generation of smart health monitoring products. This product needs to process multi-dimensional physiological data such as heart sounds, ECG, body temperature, and motion status in real-time and perform AI algorithm analysis. However, the original ARM+DSP architecture solution had issues such as AI inference power consumption reaching 2.5W, data processing latency of 100-200ms, and surface temperature exceeding 40°C during prolonged operation, severely affecting user experience and product competitiveness,” Yang Yue cited as an example. “After comparing solutions from multiple manufacturers, the customer ultimately chose to integrate our PIMCHIP-N300 acceleration unit. The key reason was that our NPU reduced power consumption by 80% when processing the same AI algorithm compared to the original solution, and the integrated storage and computing architecture reduced data processing latency to 5-10ms, significantly improving user experience.”After 6 months of technical adaptation, prototype verification, and chip integration, the customer’s product was successfully delivered. The actual results exceeded expectations: the device’s battery life improved from the original 12 hours to 72 hours, and the real-time health monitoring function received high recognition from users, while helping the customer save about 30% on battery costs and reduce overall BOM costs by 20%. Yang Yue emphasized, “This case fully demonstrates the significant advantages of our integrated storage and computing technology in edge AI applications, not only solving the customer’s technical problems but also bringing substantial commercial value and enhancing market competitiveness.”
Innovative Solutions, Ecosystem Building, Precisely Capturing and Solving Industry Pain Points
The successful implementation of edge AI chips heavily relies on scenario demands. Regarding how to accurately capture pain points in different industries, Yang Yue mentioned, “We have established a ‘three-layer in-depth, four-dimensional analysis’ mechanism for capturing industry pain points. By conducting demand research upstream (algorithm developers), midstream (equipment manufacturers), and downstream (end users) in the industrial chain, we comprehensively assess industry needs from four dimensions: technical performance, cost control, power management, and deployment convenience. By establishing joint laboratories with leading industry enterprises and participating in standard formulation, we can accurately identify industry pain points at the early stage of technological trends.”Taking the smart security monitoring industry as an example, Pine Chip Technology discovered two core pain points through in-depth research: firstly, the ultra-low power consumption intelligent demand in battery-driven scenarios, where traditional solutions still consume hundreds of milliwatts in standby mode, leading to battery life lasting only a few days, which cannot meet the long-term monitoring needs of remote areas and temporary deployments; secondly, cost pressure, as the complexity of AI algorithms increases, the costs of traditional GPU solutions surge, making it difficult for small and medium-sized security manufacturers to bear.“Based on these pain points, we fully leverage the unique advantages of the integrated storage and computing architecture to design an innovative solution of ‘ultra-low power standby + multi-modal recognition wake-up,’” Yang Yue explained. “In standby mode, we achieve microwatt-level power control through the integrated storage and computing architecture, with the chip only maintaining basic audio and infrared sensor monitoring functions. When detecting multi-modal signals such as abnormal sounds, human heat sources, or motion trajectories, the system can wake up from standby to full-function operating mode within milliseconds.”Yang Yue further elaborated, “In terms of specific technical implementation, we integrated dedicated multi-modal signal processing units in the integrated storage and computing array, achieving ultra-low power intelligent wake-up functions through hardware acceleration of audio feature extraction, infrared image processing, and motion detection algorithms. At the same time, using mature 28nm process nodes effectively controls the actual cost of a single chip, reducing it by over 70% compared to traditional solutions.”Actual test results show that Pine Chip Technology’s chips consume microwatt-level power in standby mode, ensuring that the system’s battery life can reach over 6 months in the customer’s regular usage mode, with wake-up response time less than 10ms and multi-modal recognition accuracy exceeding 92%. This solution not only solves the battery-driven security device’s endurance problem but also helps customers quickly capture the market with a highly competitive cost advantage.Pine Chip Technology also has unique insights into building an ecosystem partner network. Pine Chip Technology constructs an ecosystem partner network through a “layered interaction, scenario binding” model. At the technical level, it establishes joint R&D mechanisms with chip manufacturing companies and algorithm suppliers to co-build technology verification platforms; at the product level, it provides equipment manufacturers with full-stack support from chips to solutions, including hardware reference designs, software toolchains, and testing services; at the market level, it collaborates deeply with system integrators to create end-to-end industry solutions for vertical fields. Through this multi-level cooperation system, it achieves full-link connectivity from technological innovation to commercial implementation.Currently, Pine Chip Technology has formed a strategic cooperation matrix covering key links in the industrial chain. On the supply chain side, it has established long-term joint development relationships with leading wafer manufacturing companies and packaging testing manufacturers; on the terminal side, it has reached deep cooperation with leading companies in consumer electronics and industrial equipment to jointly define the specifications for the next generation of products; on the AI infrastructure side, it has built a technical adaptation alliance with mainstream algorithm framework vendors and cloud service providers to promote the integration of integrated storage and computing technology with the open-source ecosystem.“These collaborations precisely address the core pain points of each link in the industrial chain,” Yang Yue stated. “When collaborating with chip manufacturing companies, we jointly optimize key aspects such as lithography precision control and memory unit consistency calibration based on the special process requirements of the integrated storage and computing architecture, significantly improving mass production yield while solving compatibility issues between new computing units and existing production lines, ensuring stable supply of high-performance chips while shortening the chip mass production cycle.”Pine Chip Technology has also collaborated with algorithm suppliers to break through the “algorithm-chip” adaptation bottleneck, addressing the issue where 90% of computing power is wasted on data movement in traditional architectures. By deeply binding customized instruction sets with algorithm operators, it significantly improves effective computing power utilization; at the same time, it develops cross-framework migration tools to solve the adaptation challenges of mainstream algorithms on integrated storage and computing chips, significantly enhancing AI processing efficiency.Collaboration with equipment manufacturers and system integrators focuses on overcoming scene implementation barriers. Yang Yue stated, “We provide equipment manufacturers with modular hardware interfaces and power adjustment SDKs, solving the power stability and spatial adaptation issues of chips in different terminals such as smart headphones and industrial sensors. In addition, we work with system integrators to develop industry-specific firmware, shortening the deployment and debugging cycle of products in various industries and accelerating the transformation of technical solutions into commercial applications.”
Commercialization Barriers and Technological Evolution Trends of Edge AI Chips
The large-scale commercialization of edge AI chips faces several core obstacles: firstly, the contradiction between cost and ROI. Currently, the design, manufacturing, and deployment costs of AI chips are relatively high, while many application scenarios have long commercial return cycles, making it difficult for companies to see clear investment returns in the short term, which significantly restricts the enthusiasm for large-scale commercialization.Secondly, there are technical barriers and talent shortages. Edge AI chips involve multiple professional fields such as algorithm optimization, hardware design, and software development, and companies often lack composite talents who understand both AI algorithms and chip architecture, while the development toolchain is not sufficiently mature, increasing the difficulty and cycle of product development.Thirdly, there are issues of standardization and ecological collaboration. Different manufacturers have varying chip architectures, interface standards, and software ecosystems, leading to high system integration complexity and poor interoperability, posing significant technical risks and migration costs for companies when choosing solutions.From an industry perspective, how should these obstacles be addressed? Yang Yue believes, “It is necessary to establish a unified industry standard and certification system to reduce integration difficulties between products from different manufacturers; improve open-source toolchains and development ecosystems to lower technical barriers; strengthen industry-university-research cooperation to cultivate more composite talents; and promote upstream and downstream collaboration in the industrial chain to reduce overall costs through economies of scale. Only through industry collaboration can we truly promote the large-scale commercialization of edge AI chips.”To address these challenges, Pine Chip Technology’s actions mainly include: optimizing chip architecture design and applying high-performance traditional processes to reduce costs; strengthening software development, improving software toolchains and ecosystems; and actively participating in industry standard formulation.Yang Yue believes that in the next 3-5 years, edge AI chips will exhibit several trends: firstly, multi-modal AI integration will become mainstream. Single-modal AI applications will evolve towards multi-modal integration. The collaborative processing of visual, audio, text, and sensor data will become standard, requiring chips to possess stronger heterogeneous computing capabilities and cross-modal data processing efficiency.Secondly, lightweight deployment of large models at the edge. With the rapid development of large language models and multi-modal large models, efficiently deploying lightweight large models on edge devices will become a key challenge. This requires chips to maintain low power consumption while having the capability to handle complex inference tasks.Thirdly, the demand for ultra-low power consumption and long battery life is surging. IoT devices, wearable devices, and smart home applications have increasingly stringent power requirements, with milliwatt-level or even microwatt-level power control becoming a basic requirement, and battery life needing to reach monthly or even yearly levels.Fourthly, real-time requirements continue to rise. Key applications such as autonomous driving, industrial control, and medical monitoring have further reduced tolerance for latency, evolving from millisecond-level to microsecond-level, placing higher demands on the real-time response capabilities of chips.Fifthly, cost pressure and large-scale deployment. As AI applications become more widespread, cost will become a decisive factor, and chips need to achieve lower unit costs while ensuring performance to support large-scale commercial deployment.In response to this, Pine Chip Technology has its own strategic layout. Pine Chip Technology will continue to deepen the technology of integrated storage and computing architecture, focusing on breakthroughs in three directions: firstly, enhancing the density and efficiency of integrated storage and computing arrays; secondly, developing flexible computing units that support multiple precisions and data types to meet the precision requirements of different AI algorithms; thirdly, building hardware-level multi-modal data fusion processing capabilities to achieve efficient collaborative processing of visual, audio, and sensor data through dedicated cross-modal feature extraction and fusion units.At the same time, based on its ultra-low power advantages, Pine Chip Technology will also focus on three high-growth tracks: firstly, applications in the smart IoT field such as environmental monitoring and tracking, aiming for yearly battery life; secondly, continuous monitoring devices in the healthcare field, focusing on real-time AI analysis of physiological signals; thirdly, edge intelligence in Industry 4.0 scenarios, focusing on predictive maintenance and quality inspection applications.“The core goal of these strategic layouts is to ensure that we maintain a leading position in technological evolution over the next 3-5 years, not only to continue breaking through technical indicators but also to form competitive barriers in commercialization and ecosystem building, ultimately achieving a transition from technological leadership to market leadership,” Yang Yue emphasized.
Final Thoughts
As a core component driving the intelligent transformation of various industries, edge AI chips have broad development prospects. However, in the face of multiple challenges such as low power consumption, high performance, and low cost, the industry needs continuous innovation and collaboration. With its technological advantages in integrated storage and computing architecture, Pine Chip Technology has made significant breakthroughs in the field of edge AI chips, providing the industry with efficient and low-power solutions. In the future, as trends such as multi-modal AI integration and lightweight deployment of edge large models accelerate, edge AI chips will usher in even broader development space. Pine Chip Technology will continue to delve into integrated storage and computing technology, contributing to the construction of an intelligent future world.
Disclaimer: This article is originally from Electronic Enthusiasts, please indicate the source above when reprinting. For group communication, please add WeChat elecfans999, for submission, interview requests, please send an email to [email protected].
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