Our Technologies

Chiplet Disaggregation

Modern semiconductor systems are rapidly moving away from monolithic SoCs toward chiplet-based architectures. Chiplet disaggregation enables complex systems to be partitioned into smaller, functionally optimized dies that can be independently designed, manufactured, and integrated.

This approach improves yield, reduces cost, and allows mixing of process nodes—combining leading-edge compute chiplets with mature-node I/O or analog components.

VNCHIP Labs Expertise

At VNCHIP Labs, we specialize in architecting chiplet-based systems that balance performance, power, and scalability. Our expertise spans die partitioning, inter-chiplet communication, memory hierarchy design, and packaging-aware architecture. We help customers transition from traditional SoC designs to modular, reusable chiplet platforms that can scale across product generations.

Semiconductor Packaging and Chiplets
High Speed Interconnect Technology

Interconnect Standards

A robust chiplet ecosystem depends on standardized, high-performance interconnects. Emerging standards such as UCIe and BoW are enabling interoperable, multi-vendor chiplet integration with well-defined electrical, protocol, and software layers.

VNCHIP Labs brings deep expertise in designing and integrating interconnect fabrics across multiple abstraction levels—from PHY and link layers to protocol adaptation (e.g., AXI, CHI). We help customers evaluate trade-offs between latency, bandwidth, power efficiency, and implementation complexity, while ensuring compliance with industry standards. Our team also supports custom extensions and optimized mappings tailored to specific workloads, including AI and high-performance computing systems.

AI-Centric SoC Building

AI workloads are reshaping how SoCs are architected—from compute topology to memory bandwidth and data movement. Building an AI-optimized SoC requires a holistic approach that integrates compute engines, memory subsystems, interconnect fabrics, and software-aware design.

Strategic Integration

At VNCHIP Labs, we design AI-centric SoCs that are optimized for both training and inference workloads. Our approach emphasizes efficient dataflow, memory locality, and scalable interconnects to eliminate bottlenecks.

We integrate CPUs, GPUs, NPUs, and domain-specific accelerators into cohesive systems, supported by high-bandwidth memory and intelligent caching strategies. By aligning hardware architecture with AI model characteristics, we enable customers to achieve superior performance-per-watt and faster time-to-market.

AI Neural Network Hardware Acceleration

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