The Warp Speed project aimed at revolutionizing front-end RTL design workflows through AI-driven automation.
Faced with exponential growth in design complexity, an aging workforce, and increasing time-to-market pressures, we sought to scale productivity by orders of magnitude—not by adding more engineers, but by leveraging compute in the form of LLMs, LangChain automation, and RAG using LlamaIndex.
By shifting from a manual, engineer-driven process to a compute-driven, AI-augmented workflow, we enable:
• Faster chip design cycles (from a year to 3-months)
• Efficient automation of Verilog generation, compilation, verification, and documentation
• A natural language interface to automate engineering tasks
This transformation redefines engineering productivity, making chip deliverables scale as a function of compute, not headcount.
I served as the AI Strategist & Lead Architect, responsible for
• Developing the AI product strategy for integrating LLMs into RTL automation workflows
• Designing LangChain powered automation pipelines for Verilog generation, compilation, and verification
• Defining LLM inference server requirements to ensure scalability and security
• Creating human-in-the-loop validation mechanisms to maintain high accuracy
• Validation of LLM capabilities for front-end RTL design
• Investigation into LangChain for controlling tools and reading logs
• Benchmarking LLM-generated Verilog against human-written code
• Scalability studies (LLM utilization, parallel design efficiency)
• Research into internal private AI cloud capabilities with partners
• LLM Frameworks: Open-source models (LLamaIndex, LangChain)
• Hardware Design: Verilog, ASIC design toolchains (e.g., Synopsys, Cadence)
• AI Infrastructure: Internal LLM inference servers for secure, high-context processing
• Automation & Orchestration: Containerized workflows for nightly testing
The Warp Speed project aimed at revolutionizing front-end RTL design workflows through AI-driven automation.
Faced with exponential growth in design complexity, an aging workforce, and increasing time-to-market pressures, we sought to scale productivity by orders of magnitude—not by adding more engineers, but by leveraging compute in the form of LLMs, LangChain automation, and RAG using LlamaIndex.
By shifting from a manual, engineer-driven process to a compute-driven, AI-augmented workflow, we enable:
• Faster chip design cycles (from a year to 3-months)
• Efficient automation of Verilog generation, compilation, verification, and documentation
• A natural language interface to automate engineering tasks
This transformation redefines engineering productivity, making chip deliverables scale as a function of compute, not headcount.
I served as the AI Strategist & Lead Architect, responsible for
• Developing the AI product strategy for integrating LLMs into RTL automation workflows
• Designing LangChain powered automation pipelines for Verilog generation, compilation, and verification
• Defining LLM inference server requirements to ensure scalability and security
• Creating human-in-the-loop validation mechanisms to maintain high accuracy
• LLM Frameworks: Open-source models (LLamaIndex, LangChain)
• Hardware Design: Verilog, ASIC design toolchains (e.g., Synopsys, Cadence)
• AI Infrastructure: Internal LLM inference servers for secure, high-context processing
• Automation & Orchestration: Containerized workflows for nightly testing
• Validation of LLM capabilities for front-end RTL design
• Investigation into LangChain for controlling tools and reading logs
• Benchmarking LLM-generated Verilog against human-written code
• Scalability studies (LLM utilization, parallel design efficiency)
• Research into internal private AI cloud capabilities with partners
The Warp Speed project aimed at revolutionizing front-end RTL design workflows through AI-driven automation.
Faced with exponential growth in design complexity, an aging workforce, and increasing time-to-market pressures, we sought to scale productivity by orders of magnitude—not by adding more engineers, but by leveraging compute in the form of LLMs, LangChain automation, and RAG using LlamaIndex.
By shifting from a manual, engineer-driven process to a compute-driven, AI-augmented workflow, we enable:
• Faster chip design cycles (from a year to 3-months)
• Efficient automation of Verilog generation, compilation, verification, and documentation
• A natural language interface to automate engineering tasks
This transformation redefines engineering productivity, making chip deliverables scale as a function of compute, not headcount.
I served as the AI Strategist & Lead Architect, responsible for
• Developing the AI product strategy for integrating LLMs into RTL automation workflows
• Designing LangChain powered automation pipelines for Verilog generation, compilation, and verification
• Defining LLM inference server requirements to ensure scalability and security
• Creating human-in-the-loop validation mechanisms to maintain high accuracy
• LLM Frameworks: Open-source models (LLamaIndex, LangChain)
• Hardware Design: Verilog, ASIC design toolchains (e.g., Synopsys, Cadence)
• AI Infrastructure: Internal LLM inference servers for secure, high-context processing
• Automation & Orchestration: Containerized workflows for nightly testing
• Validation of LLM capabilities for front-end RTL design
• Investigation into LangChain for controlling tools and reading logs
• Benchmarking LLM-generated Verilog against human-written code
• Scalability studies (LLM utilization, parallel design efficiency)
• Research into internal private AI cloud capabilities with partners
Objectives:
• Automate front-end RTL design, verification, and documentation
• Reduce engineering effort per chip design
• Transition from engineering-driven to compute-driven workflows
• Accelerate customer feedback loops with overnight turnaround for any design changes
• Scale chip design deliverables beyond traditional engineering limits
Constraints:
• LLM Verilog generation accuracy must compile and pass verification
• AI-based automation pipelines must integrate with legacy toolchains
• Compute-resource scaling must be cost-effective
• Human-in-the-loop checkpoints required for mission-critical verification
• AI Strategy & Automation Team (LLM model selection, LangChain integrations)
• Chip Design Engineers (Workflow validation, human-in-the-loop testing)
• Product & Business Leadership (Defining ROI metrics, scaling strategies)
• IT & Security (Ensuring compliance with internal IP protections)
• Private AI Cloud Gurus: Inference Server Investigation and Setup, RAG reference designs
The Problem:
• Chip design cycles were slow, constrained by manual engineering workflows
• Engineers were overloaded, with increasing complexity & time-to-market demands
• Design methodologies hadn’t evolved, failing to leverage disruptive AI advancements
The Impact:
• By proposing to automate RTL workflows with AI, we gained approval for the project and the work is on going.
• I was offered a job as lead AI architect and to manage extra engineering resources allocated after we got through the crawl phase of the project.
Step 1: AI Model Validation
• Evaluated LLM-generated Verilog for accuracy, syntax correctness, and functional equivalence
• Selected LLamaIndex for RAG to integrate past designs into AI’s knowledge base
Step 2: LangChain Workflow Integration
• Implemented automated toolchains for Verilog compilation, testing, and verification
• Set up structured log parsing to extract actionable insights from design tools
Step 3: Human-in-the-Loop Review
• Introduced human validation checkpoints for high-stakes designs
• Optimized error correction cycles by combining LLM + engineer oversight
Step 4: Scalability & Compute Efficiency
• Benchmarked compute-to-output ratios to track automation efficiency
• Designed compute-scaling workflows, ensuring cost-effective resource allocation
• Gained access to an internal GPU inference server for testing.
• Evaluated engineering Gantt charts to assess the feasibility of getting chip lead times down to 3 months.
• Tested LLAMA LLMs to see if they could stitch together top-level Verilog net lists.
• Execution and implementation is still on going.
• The project proposal was approved, and we are working towards the outcomes and impact.
• AI is no longer a tool—it’s a design partner in semiconductor workflows
• Natural language automation unlocks a paradigm shift in front-end RTL workflows
• Compute-driven design scales faster than engineering-limited approaches
• Warp Speed sets the foundation for AI-driven RTL workflows in high-speed chip design
• The AI-powered automation strategy is replicable across semiconductor firms
• The case study serves as a blueprint for future AI-integration in hardware design
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