Bridging Probabilistic AI and Deterministic Hardware Correctness
The semiconductor industry faces a critical paradox: LLMs accelerate RTL generation, but hallucinations cause $10M+ silicon respins. Veriprajna's Neuro-Symbolic AI fuses the creative power of Large Language Models with the mathematical rigor of Formal Verification.
In hardware design, syntax is not semantics, and plausibility is not correctness. We don't just generate code—we prove it correct before tape-out.
Veriprajna serves fabless semiconductor companies, IP vendors, and R&D teams facing the economic reality that one race condition can cost more than a year's engineering budget.
You cannot patch hardware. A single logic bug in tape-out means $10M+ mask costs, 6-month delays, and 30-50% lifetime revenue loss. Veriprajna shifts verification left—catching bugs at $100 instead of $10M.
Pipeline hazards, forwarding logic bugs, and CDC violations plague custom cores. Our formal sandwich detects deadlocks in debug units and AXI starvation—bugs that bypass 10,000 simulation cycles.
Market windows last 18 months. Missing tape-out by 6 months = missing the generation. LLMs promise 5x faster RTL generation—but without verification, you're trading speed for silicon graveyard risk.
Veriprajna was founded on a painful reality: one race condition in a memory arbiter caused a $10M respin and 6-month market delay. This wasn't a failure of intelligence—it was a failure of verification methodology.
A highly competent team used LLM-assisted workflows to generate a high-speed memory interface arbiter. The code:
Six months later, first silicon arrived. Under a rare alignment of thermal throttling + high-bandwidth traffic, the arbiter deadlocked.
5nm mask set rendered useless. New masks + re-fabrication required.
Debug + fix + re-verification + re-synthesis + re-fabrication + packaging.
Missed market window = loss of 30-50% of product lifetime gross profit.
This exact bug would have been caught in minutes with formal verification. Our SMT solver automatically detects:
In semiconductor design, the cost of a bug increases by 10x at each stage of the design lifecycle. This exponential escalation makes post-silicon bugs existential threats.
| Design Stage | Detection Method | Cost to Fix | Risk Profile |
|---|---|---|---|
| RTL Design | Designer Inspection / Linting | ~$100 | Negligible |
| Block Verification | Unit Simulation / Directed Tests | ~$1,000 | Low |
| System Verification | Full-Chip Emulation / Regression | ~$10,000 | Moderate |
| Post-Silicon (Lab) | Validation Boards / Logic Analyzers | ~$10,000,000+ | Catastrophic |
| In the Field | Customer Return / Recall | ~$100,000,000+ | Existential |
"Wrapper" solutions (GPT-4 + Verilog system prompt) operate at RTL Design stage only. They increase velocity of code generation without increasing rigor of verification.
Result:
Subtle bugs bypass Block and System Verification → manifest in Post-Silicon stage → $10M+ cost
We shift verification left. By integrating formal verification directly into the generation loop, we force discovery of deep logic bugs at the $100 stage.
Result:
Race conditions, deadlocks, protocol violations caught before synthesis → prevents $10M liabilities
If LLMs can pass the Bar Exam, why do they fail catastrophically at chip design? The answer lies in the fundamental divergence between software and hardware description languages.
LLMs are trained on Python/Java/C++ (sequential execution). Verilog is declarative and concurrent—every statement executes simultaneously. Code line order is often meaningless.
Hardware relies on strict protocols (AXI, PCIe) with complex temporal rules. LLMs "simulate understanding" via statistics—generating code that looks 90% correct but violates obscure clauses.
High-quality Verilog on GitHub is orders of magnitude smaller than Python. Much of it is student projects that violate industrial timing constraints. LLMs lack physical context (SDC files, synthesis logs).
Bug: Data moves from stage1→stage3 in ONE cycle. Non-deterministic behavior. Synthesis mismatch.
Fix: Non-blocking + SVA property. Formal solver proves correctness. Pipeline takes 2 cycles as intended.
See how a single bug's cost multiplies 10x at each stage. Adjust parameters to model your design's risk profile.
Even if Veriprajna prevents just one race condition from reaching silicon, the savings ($10M+) exceed the cost of the entire verification platform by 100x.
While LLMs operate in the domain of probability, Formal Verification operates in the domain of proof. Veriprajna bridges these worlds with Neuro-Symbolic AI.
Traditional approach: Run testbenches with thousands of test vectors. If no failures occur, assume correctness.
Analogy:
Testing a car's brakes by driving around the block 1,000 times. But what if they only fail when it's raining, 60mph, and radio is on?
Veriprajna approach: Convert design to mathematical formula. Prove correctness across ALL possible states (2^N combinations).
Analogy:
Using physics and structural engineering to calculate stress limits. Proves that under NO possible condition will brakes fail.
At the heart of Veriprajna's engine are Satisfiability Modulo Theories (SMT) solvers like Z3 and CVC5. They convert hardware to Boolean formulas and search for counter-examples.
Convert Verilog to massive Boolean formula (SAT instance) representing every gate and flip-flop.
Accept a Property (assertion) and attempt to find a Counter-Example that breaks it.
Use algebraic heuristics to search entire state space—all 2^N possible input/state combinations.
UNSAT = Proof of correctness. SAT = Bug found with counter-example trace.
Solver proves that no bug exists. The design is mathematically perfect with respect to that property.
Solver finds a specific sequence of inputs that breaks the design. Returns Counter-Example Trace.
SVA defines the "contract" for hardware behavior. Writing these assertions is notoriously difficult—which is why Veriprajna's breakthrough is using AI to write the assertions, and Formal tools to check the AI's code.
This assertion catches AXI4 protocol violations that pass simulation but cause silicon hangs.
We are not a "Copilot." We are a Neuro-Symbolic Validation Engine that ensures Correctness-by-Construction through a proprietary iterative workflow.
Fine-tuned LLM specialized on Verilog/SystemVerilog. Handles the "What"—interpreting human intent and generating initial RTL + Assertions.
SMT solver (Formal Verification engine). Handles the "How"—proving correctness. Acts as unyielding judge of Neural layer's output.
User provides specification (text, images of timing diagrams, datasheet screenshots). Spec Analyzer Agent decomposes into functional requirements.
LLM generates TWO mutually reinforcing artifacts simultaneously:
Veriprajna spins up formal verification instance. Attempts to prove Artifact A against Artifact B.
If solver finds bug (SAT), it produces waveform trace. We feed this mathematical counter-example back into the LLM.
Loop repeats automatically until design is proven correct (UNSAT). No human intervention.
Formal verification can be computationally expensive for large designs. Veriprajna uses automated abstraction techniques:
Verify glue logic while treating large sub-blocks (RAMs, ALUs) as black boxes with interface contracts.
Break valid/ready paths to verify flow control independently of data processing, reducing complexity.
Prove property for one channel of a router and mathematically induce it for all N channels.
Veriprajna's methodology applied to RISC-V processor design—a domain where even heavily scrutinized open-source cores contain bugs that only Formal Verification can find.
Core: Ibex (used in OpenTitan secure hardware root of trust)
The Bug:
Formal verification by Axiomise revealed: debug request arriving at specific cycle during branch instruction could cause core to deadlock or execute wrong instruction.
Core: PULP Platform (Parallel Ultra-Low Power)
The Bug:
AXI interconnect could starve a master indefinitely if AWVALID and AWREADY interacted in specific "busy" pattern. Classic liveness failure.
When tasked with generating an LSU, Veriprajna automatically generates and verifies assertions for:
AXI4 requirement: valid must stay high until ready.
Scoreboarding: read must return last written data.
Liveness: LSU must eventually return response.
Veriprajna is pioneering the transition from "Computer Aided Design" (CAD) to "Computer Automated Design" through multi-agent systems and knowledge-augmented generation.
Beyond single-prompt interactions to autonomous workflows. Multiple specialized agents collaborate:
Retrieval-Augmented Generation not just for code, but for domain knowledge:
LLM retrieves "Rule 34" of coding standard → ensures compliance without hallucination.
Our ultimate goal: reduce bug escape rate to near-zero for logic covered by assertions.
While analog physics will always present challenges, logic bugs become mathematically impossible:
You can use a chatbot and hope for the best.
Or you can use Veriprajna and prove it.
Complete engineering report: Neuro-Symbolic architecture, SMT solver mechanics, SystemVerilog Assertions, counter-example guided refinement, RISC-V case studies, agentic workflows, 36 academic citations.