36Kr Exclusive | Peking University-incubated, China's first native robotics "brain chip" startup secures several hundred million yuan in financing

1. Core Proposition: Solving the "Brain" Chip Trilemma

The article centers on WeFan Intelligence's mission to address a fundamental bottleneck in the robotics industry: the lack of a domestic, high-performance, and energy-efficient computing platform for a robot's central processing unit ("brain"). The core challenge is a three-way trade-off:

• High Computing Power: Required to run complex multi-modal perception, AI inference (like transformers and world models), and decision-making algorithms.
• High Energy Efficiency: Robots, especially mobile and autonomous ones, are power-constrained.
• Manageable Cost & Ecosystem: Solutions must be commercially viable and compatible with mainstream software development tools.

Currently, the market is dominated by NVIDIA's Jetson, which is powerful but expensive and faces localization and commercial deployment hurdles. Domestic alternatives are not yet mature. WeFan's strategy is to tackle this trilemma directly with its BiGPU architecture.

2. Technical Innovation: Homogeneous Fusion, Not Heterogeneous Glue

The key technical insight presented is WeFan's homogeneous fusion approach, which distinguishes it from previous attempts in the industry.

• The Problem with Past Approaches: Earlier efforts often used heterogeneous designs—essentially combining a separate Spiking Neural Network (SNN) accelerator module with a traditional NPU (Neural Processing Unit) or GPU module on the same chip. This creates two distinct systems with potentially separate instruction sets and software toolchains, increasing complexity and development costs.
• WeFan's Solution: The BiGPU: WeFan's innovation is to unify conventional GPU architectures (optimized for matrix-heavy AI workloads like GEMM) with brain-inspired computing principles within a single, coherent architecture. This is likened to integrating the two paradigms at the foundational level.
• Mechanism and Benefit: They leverage the fact that over 80% of neural network computation is matrix multiplication. By using a novel encoding method, they convert standard computations (ANN) into a form of cumulative calculation akin to SNNs. This dramatically reduces data movement and power consumption while preserving computational effectiveness. Crucially, this is achieved under a unified instruction set and software toolchain, ensuring compatibility with mainstream AI frameworks and lowering the barrier for developers.

3. Strategic Significance and Underlying Logic

The company's strategy and the investors' enthusiasm are built on several logical pillars:

• Future-Proofing with Dual Compatibility: WeFan's platform isn't just for today's dominant transformer-based models; it's designed to natively run both classical ANNs and next-generation SNNs, as well as hybrid models. This positions the chip as a long-term platform that can evolve with AI paradigms, potentially towards Artificial General Intelligence (AGI).
• "Using Next-Generation Tech to Solve Today's Problems": This quote from the founder captures the dual logic. The team uses mature insights from brain-inspired computing (a field with long-term AGI promise) to achieve concrete, near-term benefits in power efficiency for current robotic tasks. It's an evolutionary approach rather than a revolutionary bet.
• Leveraging Deep, Cross-Disciplinary Expertise: The technical difficulty is highlighted as understanding both brain-inspired computing and conventional GPU architecture deeply enough to fuse them at the silicon level. The team's pedigree—founded by a veteran with experience at IBM, GlobalFoundries, and leading domestic firms, and backed by members from top tech companies—lends credibility to their ability to execute this complex cross-disciplinary task.
• Alignment with National Strategic Goals: The investor commentary explicitly frames the investment within the context of technological self-reliance and security ("自主可控"). Developing a domestic, high-performance alternative to foreign AI chips is a strategic imperative. WeFan's work is thus positioned not just as a commercial opportunity, but as a contribution to securing China's critical infrastructure in the AI and robotics era.

4. Deeper Implications and Market Outlook

The article and the cluster of investors point to broader industry trends:

• The Shift to Efficient, Edge Intelligence: As noted by investor Han Nan, the global AI narrative is shifting from "high-power, large-scale computing" (data centers) to "ultra-low-power edge intelligence." Robotic brains are the ultimate edge devices, making energy efficiency paramount. WeFan's approach directly targets this macro-trend.
• Embodied Intelligence as the Next Frontier: Embodied AI—the