VeriGen, Frontier LLMs, and Synthesizable RTL Edge Cases

Ask a frontier general-purpose LLM to write an asynchronous FIFO in SystemVerilog, and it will hand you something that looks clean. It will define the ports, instantiate registers, write a neat always_comb block, and even calculate the empty and full flags.

Then you take that code and run it through Verilator or Synopsys Design Compiler.

The simulation hangs on a delta-cycle race. The lint engine throws three dozen fatal warnings about blocking assignments used in sequential blocks. The formal verification tool flags a metastable transition because the read pointer was passed directly across clock domains using raw binary arithmetic instead of a Gray code sequence. Worst of all, the synthesis tool synthesizes latch inferencing across a four-way handshake because the LLM forgot to assign default values across every branch of a case statement.

This is the reality of using general-purpose language models for hardware description. General coding models know Python, Rust, and JavaScript exceptionally well. They understand sequential execution. They know that a = b + 1 happens before c = a * 2.

Digital hardware does not work that way. Hardware is concurrent. Signals evaluate in parallel across an IEEE 1800 stratified event queue. Clocks drift. Resets have recovery and removal timing constraints. Wires have propagation delays.

When researchers at NYU Tandon published VeriGen, an open-weights model fine-tuned specifically on Verilog corpora, they highlighted this structural disconnect. General-purpose models break down on hardware edge cases not because they lack parameters, but because their training data treats hardware description languages as just another dialect of imperative software.

The Event Queue and the Non-Blocking Illusion

The fundamental failure mode of general-purpose LLMs in RTL generation comes down to assignment semantics. In Verilog, the distinction between a blocking assignment (=) and a non-blocking assignment (<=) is not a stylistic choice. It dictates how the simulator schedules updates in the active, inactive, and non-blocking assignment (NBA) regions of the simulation cycle.

General models routinely mix blocking and non-blocking assignments inside the same sequential block:

always @(posedge clk or negedge rst_n) begin
    if (!rst_n) begin
        state = IDLE;
        out_reg <= 8'h00;
    end else begin
        state = next_state;
        out_reg <= next_out;
    end
end

This code compiles in simple tools. It might even pass a naive testbench. But in gate-level simulation with real back-annotated SDF timing, mixing assignment types within a clocked block creates non-deterministic behavior.

If another module samples state on the same clock edge, the simulation outcome depends entirely on how the simulator orders its internal event queues. A commercial lint rule, such as SpyGlass STARC05-2.1.5.3 or Verilator's BLKANDNBLK warning, will immediately fail this block.

VeriGen avoids this pattern because its pretraining and fine-tuning datasets were filtered against strict syntax and synthesizable coding guidelines. By training on corpora where sequential blocks exclusively use non-blocking assignments and combinational blocks exclusively use blocking assignments, the token prediction distribution aligns with hardware execution rules rather than software execution order.

// Synthesizable, deterministic pattern learned by specialized models
always_ff @(posedge clk or negedge rst_n) begin
    if (!rst_n) begin
        state   <= IDLE;
        out_reg <= 8'h00;
    end else begin
        state   <= next_state;
        out_reg <= next_out;
    end
end

This distinction seems basic. Yet frontier models with hundreds of billions of parameters still fail it regularly when generating complex state machines or pipelined datapaths.

Clock Domain Crossing and Pointer Synchronization

Multi-clock design is where general LLMs fail most dangerously. When moving data between two asynchronous clock domains, a software-centric model treats the problem like a thread synchronization lock. It assumes a simple handshake or a register transfer will suffice.

Consider a dual-clock FIFO. To generate empty and full flags, the read and write pointers must be compared across domains.

If you increment a standard binary pointer from 3 (011) to 4 (100), all three bits toggle simultaneously. In physical silicon, these three bits do not arrive at the receiving flip-flops at the exact same picosecond.

Due to physical routing differences and process variations, the receiving clock domain might sample an intermediate state like 000 or 111. If that happens, your FIFO empty flag de-asserts prematurely. Data corrupts silently. The chip locks up on the tester.

General-purpose models almost always write binary pointer synchronization:

// Broken pattern generated by general LLMs
always @(posedge rclk or negedge rst_n) begin
    if (!rst_n) begin
        wptr_sync_r1 <= 0;
        wptr_sync_r2 <= 0;
    end else begin
        wptr_sync_r1 <= wptr_bin; // Binary pointer sampled directly
        wptr_sync_r2 <= wptr_sync_r1;
    end
end

A design engineer knows this requires Gray coding. In Gray code, only one bit changes per count step. If the destination clock samples during a transition, it can only see either the old value or the new value. It can never see an invalid intermediate state.

// Correct Gray code conversion and synchronization
wire [ADDR_WIDTH:0] wptr_gray = wptr_bin ^ (wptr_bin >> 1);

always_ff @(posedge rclk or negedge rst_n) begin
    if (!rst_n) begin
        wptr_gray_sync1 <= '0;
        wptr_gray_sync2 <= '0;
    end else begin
        wptr_gray_sync1 <= wptr_gray;
        wptr_gray_sync2 <= wptr_gray_sync1;
    end
end

NYU Tandon's work on VeriGen demonstrated that fine-tuning on real hardware codebases drastically improves the model's likelihood of emitting Gray-coded pointer structures and multi-stage synchronizers with appropriate ASYNC_REG synthesis attributes. The model learns the structural idioms of RTL rather than trying to optimize for algorithmic brevity.

FSM Reset Recovery and Unreachable State Handling

Finite state machines (FSMs) represent the control backbone of digital design. An FSM in an ASIC or high-reliability FPGA must handle two physical realities: reset release timing and single-event upsets (SEUs).

General-purpose LLMs favor high-level behavioral constructs. When asked for a state machine, they often write single-process FSMs using initial blocks or synchronous resets that ignore recovery and removal constraints.

// Problematic FSM style frequently output by general LLMs
always @(posedge clk) begin
    case (state)
        IDLE: if (start) state = READ;
        READ: if (ready) state = WRITE;
        WRITE: state = IDLE;
    endcase
end

This pattern has three fatal flaws for tapeout:

1. It infers latches for unassigned conditions because there is no default state handling.
2. It lacks any asynchronous reset capability, making cold-boot initialization non-deterministic in standard-cell ASICs.
3. It uses a single process for next-state logic and state storage, which prevents the synthesis tool from optimizing register placement separately from combinational decoding.

In contrast, models trained on verified RTL produce standard two-process or three-process Moore and Mealy state machines. They properly separate combinational transition logic from sequential state registers. They include explicit default statements that force the state machine back to a safe reset state if an alpha particle flips a configuration bit in a radiation environment or noisy industrial rail.

// Two-process synthesizable FSM
typedef enum logic [1:0] {
    STATE_IDLE  = 2'b00,
    STATE_READ  = 2'b01,
    STATE_WRITE = 2'b10,
    STATE_ERROR = 2'b11
} state_t;

state_t current_state, next_state;

always_ff @(posedge clk or negedge rst_n) begin
    if (!rst_n) begin
        current_state <= STATE_IDLE;
    end else begin
        current_state <= next_state;
    end
end

always_comb begin
    next_state = current_state;
    case (current_state)
        STATE_IDLE: begin
            if (start) next_state = STATE_READ;
        end
        STATE_READ: begin
            if (ready) next_state = STATE_WRITE;
        end
        STATE_WRITE: begin
            next_state = STATE_IDLE;
        end
        default: begin
            next_state = STATE_IDLE;
        end
    endcase
end

This is not just cleaner code. It is the difference between a design that cleanly closes timing at 800 MHz and one that fails formal verification on cycle zero.

The Synthesizable Subset Problem

Language models trained on the open internet scrape everything from academic Verilog-95 testbenches to modern SystemVerilog-2017 verification environments using UVM.

This is toxic for RTL generation. In verification, you use dynamic arrays, $display, fork-join threads, #10 delay primitives, and class-based test fixtures. None of these constructs are synthesizable. You cannot map a class instance to standard cells in a TSMC N16 or GlobalFoundries 22FDX process.

When a general-purpose model is prompted to generate RTL, it frequently hallucinates non-synthesizable verification constructs inside logic modules:

• It inserts #delays to resolve race conditions instead of properly pipelining the datapath.
• It uses initial blocks to initialize memory arrays instead of generating reset loops or inferring proper FPGA Block RAM primitives.
• It attempts dynamic array slicing where the slice width is determined by a variable rather than a compile-time constant.
• It uses unsupported SystemVerilog-2012 interface constructs that crash older, battle-tested synthesis toolchains like Synopsys Design Compiler 2018 or Quartus Prime 18.1.

VeriGen tackled this by filtering training data against synthesis checkers. If a module cannot be parsed by an open-source synthesis tool like Yosys or linted cleanly by Verilator, it has no business being in the training dataset for an RTL generation model.

By restricting the output distribution to a strict synthesizable subset (Verilog-2001 or standard synthesizable SystemVerilog-2009), specialized models guarantee that the generated code actually targets physical flip-flops, LUTs, and multiplexers.

Benchmark Comparison: VeriGen vs Frontier General Models

When evaluated on benchmarks like RTLLM and VerilogEval, the differences between general coding models and hardware-specialized models become measurable.

These numbers illustrate why raw parameter scale does not solve domain-specific physical constraints. A 16-billion parameter model trained strictly on validated RTL will consistently beat a 400-billion parameter general model on functional compilation and formal equivalence tests.

Linting Rules That Specialized Models Respect

To understand why specialized training works, look at the lint rules that typical commercial synthesis engines enforce.

When an EDA tool processes a design, it runs hundreds of semantic checks before it places a single standard cell. Here are the common rules where general-purpose models fail and specialized models succeed:

1. Inferred Latches (Synopsys ELAB-980 / Verilator LATCH)

When an always_comb block or standard always @(*) block assigns a variable in one branch of an if-else tree but misses another, the synthesis tool must preserve the previous state. It inserts an asynchronous level-sensitive latch. In synchronous ASIC design, unintended latches are critical timing hazards. Specialized models learn to assign default values to all outputs at the top of combinational processes, eliminating latch inference entirely.

2. Multi-Driven Nets (Verilator MULTIDRIVEN)

General LLMs frequently assign to the same wire or reg from two different always blocks. In software, writing to a variable from two locations is normal. In hardware, this creates electrical contention on the physical metal trace. Specialized models understand that each signal must have exactly one driving process.

3. Bit-Width Mismatches (Verilator WIDTH / WIDTHTRUNC)

Assigning a 16-bit accumulator to an 8-bit bus without explicit truncation or sign extension will trigger warnings across all strict linters. General models frequently truncate silently, assuming the compiler will cast types safely like in C. Specialized models explicitly slice buses (e.g., data_out <= accum[7:0];), making truncation intent clear.

Where Hardware AI Fits in Current Workflows

None of this means specialized RTL models are ready to design an entire PCIe Gen 5 controller from scratch. They are not.

Hardware engineering is unforgiving. In software, a runtime bug can be patched with an over-the-air update within an hour. In silicon, a functional bug or timing violation that makes it past tapeout costs millions of dollars and six months of fabrication mask delays.

Because of this asymmetry, automated RTL generation is practical today for small, bounded tasks rather than end-to-end architecture.

Engineers get the highest leverage by deploying models for:

• Parameterized register files and AXI4-Lite slave decoders.
• Bit-manipulation blocks (CRC generators, parity trees, Gray encoders/decoders).
• Standard pipeline stages with backpressure handling (skid buffers, ready/valid handshake slices).
• Dual-port synchronous RAM wrappers with byte-enable write masking.
• Scaffolding SystemVerilog property assertions (SVA) for formal verification testbenches.

At Silicode (silicode.ai), we see this pattern daily: models that generate code inside strict, linter-enforced sandboxes provide immediate value, while unstructured prompts fed into generic conversational LLMs generate technical debt that takes longer to debug than writing the RTL by hand.

Building an Automated Verification Gate for LLM Code

If your team plans to use generated Verilog in real workflows, do not trust model output directly. You must wrap any LLM in a local, deterministic verification harness.

Here is a minimal, automated validation pipeline you can run locally in continuous integration:

#!/usr/bin/env bash
set -e

MODULE_NAME="fifo_cdc"
SRC="${MODULE_NAME}.sv"

echo "[1/3] Running Verilator Strict Lint..."
verilator --lint-only -Wall -Werror-PINMISSING -Werror-IMPLICIT \
          -Werror-LATCH -Werror-MULTIDRIVEN -Werror-CASEINCOMPLETE \
          --sv "${SRC}"

echo "[2/3] Checking Synthesizability with Yosys..."
yosys -p "read_verilog -sv ${SRC}; hierarchy -check -top ${MODULE_NAME}; proc; opt; memory; opt; synth -top ${MODULE_NAME}" > /dev/null

echo "[3/3] Checking for Inferred Latches..."
if grep -i "latch" yosys_synth.log; then
    echo "ERROR: Unintended latches inferred during synthesis!"
    exit 1
fi

echo "SUCCESS: Code passed lint and synthesis checks."

This basic shell script takes less than two seconds to execute. By feeding the error logs back into the LLM context window when a step fails, you create a self-correcting loop that forces the model to fix its own syntax and lint issues before an engineer ever reads the file.

What to Watch Next

The gap between general LLMs and specialized models like VeriGen shows that pretraining data quality matters infinitely more than parameter count for physical engineering disciplines.

Watch for the emergence of hybrid models that combine transformer-based token prediction with embedded formal verification engines. The next real advance in RTL generation will not come from models with 2 trillion parameters scraped from raw web pages. It will come from smaller, domain-specific models trained directly against formal property solvers, timing closure feedback, and PDK cell libraries.

If you are evaluating AI tools for your RTL workflow today, run them through your strictest lint configuration on day one. Test them on a multi-clock FIFO with Gray pointers and an asynchronous active-low reset. If the model produces blocking assignments in sequential blocks or drops raw binary counters across clock boundaries, you know immediately whether it was built for hardware or merely trained on text.