The Hardware Revolution in the AI Era: The Wave of Integration in Chips, Storage, and Energy Management đ As the scale of large model parameters breaks through the trillion level and edge AI devices permeate everyday life, the bottlenecks of traditional hardware architecture become increasingly prominentâ the separation of computing units and storage units leads to data transport energy consumption exceeding 60%, and the disconnection between energy management and core hardware results in an imbalance in the energy efficiency of AI devices. In today’s era of exponential growth in computing power demand, the high integration of chips, storage, and energy management has evolved from a technical trend to an industrial necessity, reshaping the technological foundation and industrial landscape of the AI era.
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1. The Underlying Logic of the Integration Revolution: Breaking the Triple Technical Bottleneck 1. The Von Neumann Bottleneck: The Efficiency Deadlock of Data Transport In the traditional Von Neumann architecture of computers, computing (CPU/GPU) and storage (memory/disk) are physically separated, requiring frequent data transfers between the two during AI computations. When training a model with hundreds of billions of parameters, even with HBM2e memory bandwidth reaching 1.8TB/s, it still cannot meet the real-time demands of computing units, and the energy consumed in data transport exceeds that of computation itself by more than 100 times. This inefficient mode of “data moving” has become a core obstacle to enhancing AI computing power and terminal deployment, while integrated architecture fundamentally solves the problem by bringing computation closer to data. 2. The Energy Efficiency Crisis: Energy Constraints of AI Scaling The explosive growth of AI computing power has led to astonishing energy consumptionâAI servers in data centers consume 3-5 times more power than ordinary servers, while edge devices are limited by battery capacity and cannot support high-power computations. In compute-storage integrated chips, the analog-to-digital converter (ADC), acting as the “translator” connecting analog computation and digital processing, accounts for as much as 87.8% of energy consumption, nearly offsetting the energy efficiency advantages of the architecture itself. If energy management is not deeply coordinated with chips and storage, even the strongest computing power will struggle to achieve large-scale applications, pushing integration from a “performance-oriented” approach to an “energy efficiency-oriented” one. 3. The Scene Adaptation Challenge: Hardware Challenges of Fragmented Demands From cloud-based kilowatt clusters to terminal TWS earphones, the fragmentation of AI application scenarios imposes diverse requirements on hardware: the cloud requires extreme computing power and large-capacity storage, while terminals pursue low power consumption and small size. Traditional discrete hardware struggles to meet the diverse needs of multiple scenarios, while integrated architecture achieves flexible adaptation through “customized fusion”âfor example, the SRAM compute-storage integrated NPU on the terminal side can control the power consumption of voice model inference to below 1mW, while the cloud meets the training needs of hundreds of billions of parameter models through HBM near-memory computing clusters. 2. Three Major Technical Paths and Breakthroughs in Integration 1. Compute-Storage Integration (PIM): The Ultimate Form of Storage as Computation The compute-storage integrated architecture embeds computing logic directly within storage units, completely eliminating data transport, with analog domain compute-storage integration seen as the most promising direction. Breakthrough research by a joint team from the University of Hong Kong, Hong Kong University of Science and Technology, and Xi’an University of Electronic Science and Technology utilizes the programmable characteristics of memristors to create a “smart ruler” ADC architecture, reducing the power consumption of compute-storage integrated chips by 57.2% and shrinking the area by 30.7%, solving the “energy consumption black hole” problem of traditional ADCs. In the commercialization process, Samsung’s MRAM chip achieves 98% accuracy in handwriting recognition through a “resistive summation” architecture, with power consumption only 1/10 of traditional solutions; the Apple N300’s SRAM compute-storage NPU achieves an area efficiency of 0.26TOPS/mm², with voice model inference power consumption below 1mW, showcasing the immense potential of terminal scenarios. The core advantage of such architectures lies in their hardware-native adaptabilityâby analyzing the data distribution of each layer of AI using the Lloyd-Max algorithm, customized “scaling” schemes are set to achieve dual optimization of accuracy and energy efficiency. 2. Near-Memory Computing (NMC): A Transitional Solution Compatible with Existing Ecosystems Near-memory computing shortens the physical distance between computing units and storage media through 2.5D/3D stacking technology, enhancing energy efficiency while being compatible with existing chip ecosystems. NVIDIA’s H100 adopts 2.5D packaging to integrate HBM with GPU, achieving a 5-fold increase in bandwidth compared to previous generations; Samsung inserts computing units into 3D DRAM stacking, reducing memory access power consumption by 40%. The popularization of the CXL protocol further strengthens the scalability of near-memory computing, supporting memory expansion pooling and unified addressing of TB-level “memory-disk” resources, allowing storage resources to be allocated to computing units as needed. This “incremental integration” solution, due to its low R&D risk and fast commercialization speed, has become the mainstream choice for current cloud AI hardware, with the global near-memory computing chip market expected to exceed $20 billion by 2025. 3. Heterogeneous Integration + Intelligent Energy Management: A Collaborative Paradigm for Full-Link Optimization Heterogeneous integration combines different functional chip modules (computing cores, storage units, energy efficiency controllers) through advanced packaging technology, along with intelligent energy management algorithms to achieve full-link optimization. The Sipeed TH1520 chip adopts a dual-core DSP architecture, with the signal processing core and AI acceleration core collaborating, combined with 512KB large-capacity SRAM to reduce external access by 90%, compressing typical power consumption for voice interaction to 15-80mW, only 1/5 of the industry average. The incorporation of technologies such as dynamic voltage frequency scaling (DVFS) and three-level energy management allows hardware to adapt in real-time according to scenariosâpower consumption during the listening phase is as low as 0.15mW, and non-active modules are dynamically shut down during full-function operation, achieving over 70% energy savings in products like Midea air conditioners and XPeng P7. This “hardware collaboration + algorithm optimization” integration model is becoming the core design philosophy for edge AI devices. 3. The Next Decade: Three Major Evolution Trends of Integration 1. Technical Architecture: From “Module Integration” to “Native Fusion” In the next 5-8 years, integration will evolve from the current “physical integration” to “native fusion”ânew types of storage media (memristors, graphene/perovskite synaptic transistors) will simultaneously undertake storage and computation functions, and energy management units will be deeply embedded in chip architecture, achieving native collaboration of “computing power-storage-energy efficiency”. The development of photonic interconnect technology by Stanford and MIT is expected to replace electrical signal transmission, solving the energy consumption problem of long-distance data transport, further enhancing the energy efficiency ratio of integrated architectures by an order of magnitude. On the terminal side, SRAM/RRAM compute-storage integrated NPUs will become mainstream, supporting real-time inference of lightweight models; on the cloud side, a three-tier architecture of “high-speed compute-storage units + near-memory computing pools + pooled storage” will be formed to meet the training needs of kilowatt clusters; the ultimate form will be brain-like chips that simulate synaptic connections through memristors, achieving efficient information processing similar to biological neural networks. 2. Industrial Landscape: Coexistence of Vertical Integration and Ecological Collaboration The high threshold of integrated technology will drive the industry towards vertical integrationâchip design companies will deeply participate in the R&D of storage media and optimization of energy efficiency algorithms, while terminal manufacturers will build differentiated advantages through customized integration solutions. At the same time, the improvement of open-source ecosystems such as the CXL protocol and power management SDK will lower the technical barriers for small and medium-sized enterprises, forming an industrial ecosystem of “core technology self-research + shared general modules”. Leading companies will focus on breakthroughs in underlying technologies such as 3D stacking and new types of storage, while small and medium-sized enterprises will delve into optimizing integration solutions for niche scenarios. It is expected that by 2030, the global AI integrated hardware market will exceed $150 billion, with integrated chips on the terminal side accounting for over 40%, becoming the fastest-growing segment. 3. Application Scenarios: From “Point Intelligence” to “Full Domain Intelligent Connection” The low power consumption and high computing power advantages brought by integration will drive AI from point applications to full domain intelligent connections. Terminal devices will achieve “unperceived power consumption”âsmartwatches can continuously run multi-modal AI functions, and smart homes will have annual standby power consumption of less than 1 kWh; in industrial scenarios, integrated edge chips will support real-time diagnostics and predictive maintenance, reducing energy consumption by over 50% compared to traditional solutions; wearable monitoring devices in the medical field will achieve real-time AI analysis of physiological data through highly integrated hardware, with power consumption controlled at the milliwatt level. The cloud will support real-time inference of large models with hundreds of billions of parameters through ultra-large-scale integrated clusters, reducing response latency in scenarios such as AI customer service and intelligent driving to milliseconds. The interaction between people and devices will shift from “command-triggered” to “proactive perception,” with integrated hardware becoming the core infrastructure for building an intelligent society. Tiangong Pavilion Observation
âď¸ Integration Defines the Next Generation Standard for AI Hardware The high integration of chips, storage, and energy management essentially represents a paradigm shift in AI technology from “pursuing computing power” to “pursuing efficiency.” This revolution will not only reshape the technological route and competitive landscape of the hardware industry but also provide a sustainable technological foundation for the large-scale application of AIâwhen computation is no longer limited by energy, and intelligent devices are ubiquitous yet “unperceived in power consumption,” a more efficient and intelligent era is about to arrive. For enterprises, grasping the core of the integration trend lies in balancing technological innovation with scene implementation, building core competitiveness through hardware collaborative design and algorithm optimization; for the industry, it is necessary to promote the construction of technical standards and ecosystems to avoid redundant R&D and resource waste. In this hardware revolution, only by conforming to the wave of integration can one gain a competitive edge in the AI era.
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