Pioneering modular silicon architectures, high-bandwidth interconnects, and AI-native System-on-Chip engineering.
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 silicon yields, significantly reduces total die cost, and allows mixing of process nodes — combining leading-edge compute chiplets with mature-node I/O, power management, or analog components.
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.
A robust chiplet ecosystem depends on standardized, high-performance interconnects. Emerging industry standards such as UCIe (Universal Chiplet Interconnect Express) and BoW (Bunch of Wires) 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 strict compliance with industry specifications.
Our engineering team also supports custom extensions, high-speed SerDes mapping, and low-latency bridging tailored to specific workloads, including ultra-scale AI training clusters and high-performance computing (HPC) nodes.
AI workloads are fundamentally reshaping how SoCs are architected — from compute topology and tensor acceleration to memory bandwidth and data movement. Building an AI-optimized SoC requires a holistic approach that integrates heterogeneous compute engines, memory subsystems, interconnect fabrics, and software-aware compiler co-design.
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 compute bottlenecks.
We integrate CPUs, GPUs, NPUs, and domain-specific accelerators into cohesive systems, supported by high-bandwidth memory (HBM3/LPDDR5X) 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.
