The gap between algorithms and hardware
Turning a Python algorithm into synthesizable RTL is one of the most labor-intensive tasks in hardware engineering. You go from high-level math to fixed-point arithmetic, micro-architectural decisions, pipeline staging, and finally thousands of lines of SystemVerilog — each step requiring specialized knowledge and careful verification. ARDA automates this entire flow using AI agents.
Pipeline architecture
ARDA’s pipeline mirrors how a hardware engineer actually works, broken into eight sequential stages:
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Spec — Parse the input Python algorithm and extract a formal specification: inputs, outputs, data types, computational graph, and performance requirements.
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Quant — Convert floating-point operations to fixed-point arithmetic. Determine bit widths, analyze quantization error, and ensure numerical accuracy meets the spec’s tolerance.
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MicroArch — Design the micro-architecture: pipeline depth, resource sharing strategy, memory organization, and datapath topology. This is where the agent makes the key tradeoffs between area, latency, and throughput.
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Architecture — Refine the micro-architecture into a detailed architectural specification with exact signal widths, FSM states, and timing diagrams.
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RTL — Generate SystemVerilog from the architectural spec. The output is synthesis-ready, not behavioral — proper clock domains, reset handling, and coding style for FPGA tools.
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Verification — Generate test vectors from the original Python algorithm, build a testbench, and verify functional correctness of the generated RTL.
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Synth — Run synthesis and analyze resource utilization against the FPGA budget. Flag timing violations or resource overflows.
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Evaluate — Compare the final implementation against the original spec: latency, throughput, area, and numerical accuracy.
Each stage is handled by a specialized AI agent with domain-specific tools and prompts. The agents use a confidence-based feedback loop — if an agent’s confidence in its output drops below a threshold, it iterates before passing results downstream.
Why agents, not a monolithic model
A single LLM prompt can’t reliably handle the full algorithm-to-RTL flow. The design space is too large and the constraints at each stage are too different. Quantization requires numerical analysis; micro-architecture requires area/timing tradeoffs; RTL generation requires synthesis tool compatibility.
By splitting the pipeline into specialized agents, each one operates in a focused context with relevant tools and evaluation criteria. The spec agent doesn’t need to know about FPGA resource budgets; the synth agent doesn’t need to understand quantization theory. This modularity also makes the system extensible — adding support for a new FPGA family or a different HDL is a change to one or two agents, not a rewrite of the entire pipeline.
Current status
ARDA successfully generates synthesis-verified SystemVerilog for basic algorithmic blocks. The pipeline handles the happy path well — algorithms with clean dataflow graphs, moderate complexity, and standard fixed-point requirements. More complex scenarios (irregular memory access patterns, dynamic control flow, multi-clock domains) are areas for continued development.