Preamble

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Research on the Evolution of Reconfigurable Chip Technology

(1. Huaneng Jinan Huangtai Power Generation Co., Ltd., Jinan, Shandong 250100, China)

Abstract: This paper comprehensively reviews the evolution, technical principles, market applications, development trends, and challenges of reconfigurable chip technology. With the rapid advancement of information technology, emerging applications have imposed increasingly stringent demands on chip performance, while traditional chip architectures struggle to meet these new requirements. Reconfigurable chips, with their high energy efficiency, scalability, and flexibility, have gradually become a critical solution. This article traces the development of reconfigurable chip technology from theoretical conception to industrial application, examining core technical features such as dataflow-driven architecture and multi-level reconfiguration capabilities. In market applications, reconfigurable chips have demonstrated remarkable effectiveness and broad prospects in artificial intelligence, edge computing, and data centers. Looking ahead, reconfigurable chip technology will continue to evolve toward architectural innovation, ecosystem development, integration with other emerging technologies, and specialization for vertical domains. The paper also identifies key challenges including dynamic reconfiguration speed, configuration storage and loading, ecosystem fragmentation, and high application costs, proposing countermeasures such as optimizing dynamic reconfiguration algorithms, promoting standardization, and strengthening industry-academia-research collaboration to provide a scientific basis for further research and application.

Keywords: Reconfigurable chips, Evolutionary history, New architecture chips

1.1 Project Background and Significance

As information technology advances rapidly, chips have become the foundation and core of the modern information industry, with their technical level serving as a crucial indicator of national or regional technological strength. In recent years, the emergence of artificial intelligence (AI), Internet of Things (IoT), and 5G communication has placed higher demands on chip computing power, energy efficiency, and flexibility. However, traditional architectures such as CPUs and GPUs have proven inadequate in meeting these new requirements. Reconfigurable Processing Units (RPUs), as a new class of chips capable of dynamically configuring computing resources, have gradually emerged as a key solution due to their superior energy utilization, exceptional flexibility, and strong scalability. This paper aims to comprehensively review the evolution of reconfigurable chip technology, analyze its technical principles, market applications, development trends, and challenges, and provide a scientific basis for further research and application.

1.2 Research Objectives and Tasks

The primary objectives of this study are:

1. To trace the evolution of reconfigurable chip technology: Reviewing the complete development trajectory from theoretical proposal to industrial application.
2. To analyze the technical principles: Examining core technical features such as dataflow-driven architecture and multi-level reconfiguration capabilities.
3. To explore market applications: Analyzing current applications and future prospects in AI, edge computing, data centers, and other domains.
4. To forecast development trends: Predicting future directions based on current technological trends and market demands.
5. To identify challenges and propose countermeasures: Addressing potential problems in technology development and application with corresponding solutions.

Specific tasks include analyzing domestic and international development status and trends, evaluating technical feasibility and identifying innovation points and potential risks, designing product plans with clear positioning and advantages, conducting economic feasibility analysis to predict investment returns, and formulating R&D and application plans to ensure smooth project implementation.

1.3 Research Methods and Scope

This study employs literature review, data analysis, and expert interviews to conduct a comprehensive analysis of reconfigurable chip technology, integrating domestic and international development status and trends. The research scope covers market analysis, technical feasibility assessment, product planning and design, economic feasibility analysis, and technology development roadmaps, aiming to provide a holistic feasibility evaluation for further research and application.

2.1 Definition and Characteristics of Reconfigurable Chips

Reconfigurable chips are a new class of processors capable of dynamically configuring computing resources. Their core lies in incorporating programmable processing elements and interconnect networks that can, based on specific application requirements and dataflow characteristics, dynamically configure computing units, interconnect structures, and data paths during runtime using dynamic reconfiguration technology. This enables data-driven computation approaching the efficiency of Application-Specific Integrated Circuits (ASICs).

Key characteristics include:

• High Energy Efficiency: By optimizing hardware resource utilization according to application demands, reconfigurable chips significantly improve energy efficiency ratios.
• High Scalability: Supporting multi-level reconfiguration capabilities—including computing unit reconfiguration, interconnect network reconfiguration, and storage system reconfiguration—reconfigurable chips offer excellent scalability.
• High Flexibility: Reconfigurable chips can be flexibly programmed like CPUs while maintaining ASIC-level energy efficiency, meeting diverse application requirements.

2.2 Technical Principles of Reconfigurable Chips

The technical principles of reconfigurable chips are manifested in several key aspects:

2.2.1 Dataflow-Driven Architecture

Unlike traditional CPUs that use instruction-driven models, reconfigurable chips employ a dataflow-driven architecture where hardware resources (processing elements and interconnect networks) are dynamically mapped based on dataflow characteristics. This eliminates traditional overheads such as instruction decoding and branch prediction, improving energy efficiency by up to an order of magnitude. The dataflow-driven architecture enables more efficient processing of complex algorithms and large-scale data.

2.2.2 Multi-Level Reconfiguration Capability

Reconfigurable chips support multi-granularity reconfiguration from microarchitecture to circuit level, including computing unit reconfiguration, interconnect network reconfiguration, and storage system reconfiguration. This multi-level capability allows dynamic adjustment of hardware resource utilization according to different application requirements, achieving elastic scheduling and efficient reuse of computing resources across spatial and temporal dimensions.

• Computing Unit Reconfiguration: Dynamically configuring operator functions (such as operator type and precision switching) through configuration parameters.
• Interconnect Network Reconfiguration: Dynamically adjusting connection topologies between computing units to optimize data transmission paths and improve efficiency.
• Storage System Reconfiguration: Optimizing cache allocation strategies based on data access patterns to improve storage and access efficiency.

2.2.3 Internal Structure Composition

The internal structure of reconfigurable chips primarily consists of computing arrays, reconfiguration controllers, and memory units.

• Computing Array: Composed of Processing Element (PE) arrays, where each PE possesses multiple arithmetic and logic capabilities, forming complex data paths through programmable interconnect networks.
• Reconfiguration Controller: Issues "configuration information" to dynamically adjust connection patterns and operation modes of the computing array. This separation design (independent dataflow and control flow) enables flexible programmability like CPUs while maintaining ASIC-level energy efficiency.
• Memory: Divided into configuration memory and data memory. Configuration memory stores the "configuration information" for computing arrays, while data memory stores raw data, intermediate data, and results.

3.1 Early Theoretical Research and Concept Verification

The concept of reconfigurable chips can be traced back to the 1960s, though it received limited attention due to technological constraints. In the late 1960s, American scholar Gerald Estrin first proposed the concept of "reconfigurable computing," building a prototype system composed of fixed and programmable hardware that laid the theoretical foundation. In the 1980s, high-level synthesis theory and methodology emerged as one of the core technological origins, while FPGA architecture began developing as a branch of reconfigurable computing, though applications remained limited to academic research and niche domains. During the 1990s, FPGA technology matured with companies like Xilinx driving commercialization, but reconfigurable chips were still viewed as supplementary to traditional architectures. In 1997, UC Berkeley launched the GARP project to verify reconfigurable computing architecture concepts, demonstrating advantages in performance, energy efficiency, and flexibility through a reconfigurable hardware platform and gradually attracting academic and industrial attention.

3.2 Key Technology Breakthroughs and Industrialization Exploration

Entering the 21st century, reconfigurable chip technology advanced significantly with developments in integrated circuit design and microelectronics manufacturing. In 2000, Chinese scholars achieved breakthroughs in core technologies such as dynamic reconfiguration and dataflow-driven architecture, proposing hardware-software co-design methods that endowed reconfigurable chips with "hardware dynamically changing with software" characteristics, propelling China to become a leader in this field. In 2003, MIT launched the MORPHEUS project to explore reconfigurable computing in specialized domains, successfully achieving efficient support for specific algorithms and applications through a reconfigurable hardware platform. In 2006, Tsinghua University established the Reconfigurable Computing Laboratory to study theory and architecture implementation, achieving multiple key technological breakthroughs with support from national and local governments. During the 2010s, reconfigurable chips began industrial exploration; Samsung integrated them into consumer electronics (such as 8K TVs and Exynos SoCs) to optimize video decoding and AI image enhancement functions, validating technical feasibility. In 2015, Tsinghua University's research achievements in reconfigurable chips won the second prize of the National Technology Invention Award, while the International Technology Roadmap for Semiconductors (ITRS) listed reconfigurable chips as "the most promising future chip architecture technology." In 2018, Xilinx launched the Versal series Adaptive Compute Acceleration Platform (ACAP), the first to integrate CGRA (Coarse-Grained Reconfigurable Array) IP into FPGAs, significantly enhancing DSP processing capabilities for data centers and intelligent driving applications23. In 2022, Intel integrated reconfigurable computing units into Xeon processors, improving data center energy efficiency by 40% through dynamic resource allocation, while PACT Corporation implemented reconfigurable technology in aerospace applications. By 2025, dynamic reconfiguration technology has matured to support multi-level reconfiguration from computing units to interconnect networks and storage systems, achieving tenfold energy efficiency improvements over traditional CPUs and becoming a core computing platform for AI and edge computing16.

3.3 Commercial Applications and International Competition

As reconfigurable chip technology matured, commercial applications gradually expanded. In 2017, China's State Council included reconfigurable computing in the "New Generation Artificial Intelligence Key Common Technology System" in its "New Generation Artificial Intelligence Development Plan," providing key support. That same year, a Tsinghua University team achieved breakthroughs in dynamic reconfiguration and multi-granularity fusion technologies, developing the "Thinker" series of reconfigurable AI chips. These chips demonstrated significantly higher energy efficiency than comparable GPUs when running typical AI tasks and were featured in MIT Technology Review.

In the international market, leading companies such as Xilinx, Samsung, and Intel have also positioned themselves in the reconfigurable chip domain. In 2018, Xilinx launched the landmark Versal series ACAP FPGA products, embedding advanced CGRA reconfigurable computing IP that revolutionized DSP processing capabilities. In early 2019, Tsingmicro Intelligence's first reconfigurable chip—the world's first mass-produced commercial reconfigurable chip—marked the formal entry of reconfigurable chip technology into commercial application.

3.4 Technological Innovation and Ecosystem Construction

In recent years, rapid development in AI and IoT has driven continuous innovation in reconfigurable chip technology. Technologically, reconfigurable chips have evolved from single architectures toward multi-level, multi-granularity architectures supporting dynamic reconfiguration from data level to task level. Simultaneously, deep integration with emerging technologies such as compute-in-memory and Chiplet has further enhanced performance and energy efficiency.

Regarding ecosystem construction, a complete system from algorithms to hardware has gradually formed. Numerous domestic and international companies and research institutions have launched development platforms and toolchains based on reconfigurable chips, lowering development barriers and costs. Additionally, reconfigurable chips have actively engaged with open-source communities and standardization organizations, promoting widespread adoption and innovation.

4.1 Artificial Intelligence Domain

In AI, reconfigurable chips have become a key technology for accelerating algorithm execution due to their high energy efficiency, scalability, and flexibility. They can dynamically adjust hardware resource utilization according to different AI algorithms and application requirements, achieving efficient resource utilization. For example, during deep learning training, reconfigurable chips can dynamically configure computing units and storage systems based on network structure and parameter scale, improving training efficiency and accuracy.

Currently, multiple companies have launched AI accelerators and solutions based on reconfigurable chips. For instance, Tsingmicro Intelligence's TX8 series cloud reconfigurable AI chips and SambaNova's SN40L chip systems have achieved remarkable results in image recognition, speech recognition, and natural language processing.

4.2 Edge Computing Domain

In edge computing, reconfigurable chips have become a key technology supporting intelligent edge devices due to their low power consumption, real-time performance, and flexibility. Edge devices typically operate in resource-constrained environments requiring efficient processing of large volumes of real-time data. Reconfigurable chips can dynamically adjust hardware resource utilization according to different application scenarios and data processing needs, achieving efficient resource utilization.

For example, in smart homes, reconfigurable chips can support collaborative operation of multiple intelligent devices, enabling intelligent management and control of home systems. In smart manufacturing, they can support real-time production line monitoring and fault diagnosis, improving production efficiency and product quality.

4.3 Data Center Domain

In data centers, reconfigurable chips have become a key technology for enhancing computing capability and energy efficiency due to their high performance, energy efficiency, and scalability. With rapid development in big data and cloud computing, data centers face increasing computational pressure and energy efficiency challenges. Reconfigurable chips can dynamically adjust hardware resource utilization according to different workloads and dataflow characteristics, achieving efficient resource utilization and improved energy efficiency.

For example, Intel's Xeon processor integration of reconfigurable computing units has effectively improved data center energy efficiency through intelligent dynamic resource allocation. Test data shows that power consumption per computing unit has been reduced by 40%, making significant contributions to data center energy savings and efficiency improvements.

5.1 Architecture Innovation and Performance Enhancement

Future reconfigurable chip technology will continue advancing toward architectural innovation and performance enhancement. On one hand, reconfigurable chips will adopt more advanced integrated circuit design techniques and manufacturing processes to improve performance and energy efficiency. On the other hand, they will explore multi-level, multi-granularity architectural design and dynamic heterogeneous computing modes to achieve elastic scheduling and efficient reuse of computing resources across spatial and temporal dimensions.

5.2 Ecosystem Construction and Standardization Promotion

As reconfigurable chip technology matures and commercial applications expand, ecosystem construction and standardization promotion will become critical future directions. The field will develop a complete ecosystem from algorithms to hardware, encompassing development platforms, toolchains, and application cases. Simultaneously, reconfigurable chips will actively integrate with open-source communities and standardization organizations to drive widespread adoption and innovation.

5.3 Integration with Other Emerging Technologies

Future reconfigurable chip technology will deeply integrate with emerging technologies such as compute-in-memory and Chiplet to further enhance performance and energy efficiency. Compute-in-memory technology eliminates latency and energy overhead in data transmission by tightly integrating storage and computing units, while Chiplet technology improves integration and flexibility by packaging multiple small chips into a larger one. The deep integration of these technologies with reconfigurable chips will provide new approaches for future computing architecture innovation.

5.4 Vertical Domain Specialization Evolution

As reconfigurable chip technology develops and application domains expand, vertical domain specialization will become an important trend. For domains such as autonomous driving, industrial IoT, and biological computing, reconfigurable chips will form configurable template libraries and agile development kits to accelerate algorithm-chip co-optimization and customized design. This will enhance application effectiveness and competitiveness in specific domains.

6.1 Technical Challenges and Countermeasures

Reconfigurable chip technology faces several technical challenges, including dynamic reconfiguration speed, configuration information storage and loading, and control-intensive task processing. Countermeasures include:

• Optimizing dynamic reconfiguration algorithms: Researching more efficient dynamic reconfiguration algorithms and dataflow mapping strategies to improve reconfiguration speed and efficiency.
• Adopting efficient storage technologies: Utilizing high-speed, low-power storage technologies such as SRAM and eFlash to accelerate configuration storage and loading.
• Exploring control-intensive task processing methods: Investigating parallel processing methods for control-intensive tasks on reconfigurable chips to improve processing speed and efficiency.

6.2 Ecosystem Construction Challenges and Countermeasures

Ecosystem construction for reconfigurable chip technology faces challenges such as fragmentation and poor compatibility. Countermeasures include:

• Promoting standardization: Actively participating in domestic and international standardization organizations to advance the standardization process.
• Establishing open platforms: Building open platforms from algorithms to hardware, providing unified development kits and programming interfaces to attract more developers and partners.
• Strengthening industry-academia-research collaboration: Enhancing cooperation and exchange among universities, research institutions, and enterprises to jointly drive innovation and application.

6.3 Market Application Challenges and Countermeasures

Market application of reconfigurable chip technology faces challenges such as high costs and development difficulty. Countermeasures include:

• Reducing chip costs: Lowering costs through optimized chip design and improved manufacturing processes to enhance market competitiveness.
• Providing development tools and support: Offering rich development tools and support services to lower development barriers and attract more developers.
• Expanding application domains: Actively exploring new application domains and market spaces to improve effectiveness and competitiveness across different fields.

7.1 Summary of Research Findings

This paper has comprehensively reviewed and analyzed reconfigurable chip technology from multiple perspectives including definition and characteristics, technical principles, evolution, market applications, development trends, and challenges. Key findings include:

• Reconfigurable chip technology has become a critical path for addressing traditional architecture bottlenecks due to its high energy efficiency, scalability, and flexibility.
• The technology has achieved remarkable results in AI, edge computing, and data centers, demonstrating broad market prospects.
• Future development will continue toward architectural innovation, ecosystem construction, integration with emerging technologies, and vertical domain specialization.
• Although facing numerous challenges, these can be effectively addressed through optimizing dynamic reconfiguration algorithms, promoting standardization, and strengthening industry-academia-research collaboration.

7.2 Future Outlook

Looking ahead, reconfigurable chip technology will play an increasingly important role in information technology. As the technology continues to develop and application domains expand, reconfigurable chips will become a cornerstone supporting future intelligent computing and IoT development. We must also address the challenges and problems facing the technology, actively seeking solutions to drive continuous progress and improved application effectiveness.