Reading
A multi-agent system autonomously completed the full design of VerTQ, an LLM inference accelerator, in 80 hours. It includes 5129 floating-point units and can be mapped to FPGA and ASIC.
Section 01
Design Conductor 2.0, a multi-agent system, has achieved a milestone in AI autonomous hardware design: it completed the full design of VerTQ (an LLM inference accelerator supporting TurboQuant) in just 80 hours. VerTQ includes 5129 floating-point units and can be directly mapped to FPGA and ASIC. This work marks a new level of AI's ability in complex hardware design.
Section 02
The team's previous Design Conductor 1.0 (Dec 2025) autonomously designed a Linux-compatible RISC-V CPU with 5-stage pipeline in 12h, proving AI's feasibility in hardware design. Design Conductor 2.0 represents a qualitative leap: it handles tasks 80x larger (from simple CPU to complex AI accelerator), maintains higher design quality (synthesizable/implementable), and runs fully autonomously. This progress comes from two key factors: the latest LLM model (April 2026 release) and optimized multi-agent collaboration framework.
Section 03
Multi-agent framework improvements: 1. Smarter task decomposition into parallel sub-tasks; 2. Specialized agents for architecture design, RTL coding, verification, optimization;3. Optimized info transfer and coordination between agents;4. Built-in multi-level design checks and verification.
LLM model enhancements: The April 2026 model brings longer context windows (handle large design descriptions/code), stronger reasoning (better architecture decisions), higher-quality RTL code (more standardized, easier to synthesize), and better cross-domain knowledge integration (connect algorithm and hardware).
Section 04
VerTQ Design: Targeted at LLM inference with native TurboQuant support, it has 240-cycle pipeline depth,5129 FP16/32 units,8 parallel attention pipes—all starting from the TurboQuant paper's algorithm description.
Implementation: FPGA (125MHz, verified timing convergence); ASIC (TSMC16FF process, estimated area of5.7mm², excellent efficiency).
Other cases: Design Conductor2.0 also completed 3 other designs across different domains, showing versatility.
Section 05
Efficiency: Though token usage is high, the system offers significant benefits:80h vs months of traditional design, reduced engineer workload, fast iteration of multiple designs—making it economically advantageous even with API costs.
Limitations:1. Complex timing convergence may need human help;2. Power optimization room exists;3. Backend processes (layout/routing) have limited automation;4. Verification coverage (formal verification, full testing) needs improvement.
Section 06
Industry Impact: Potential paradigm shift in hardware design—democratization (lower entry barrier), fast prototyping (shorter concept-to-prototype cycle), architecture exploration (systematic search of design space), custom chips (more feasible for specific workloads).
AI Infra Significance: Auto-design of specialized accelerators like VerTQ will boost LLM inference efficiency; AI designing AI hardware creates a flywheel effect.
Research Directions: Multi-agent collaboration, deep integration with EDA tools, long-term planning ability, effective encoding of domain knowledge.
Section 07
Design Conductor2.0 is a major breakthrough in AI autonomous hardware design. Combining multi-agent collaboration and advanced LLM models, it completed VerTQ's design in80h. This achievement shows AI's potential in complex engineering tasks and opens new possibilities for hardware design automation and democratization. Despite limitations, AI-assisted and autonomous design will play an increasingly important role in future hardware development.
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