Rise Agentic AI
From system intent to production-ready RTL, every step grounded in hardware knowledge and verified by real tools.
Rise Agentic AI is the industry’s first deterministic Native-AI architecture that transforms structured design specifications into production-ready RTL, verification environments, and software-ready models in a closed-loop system. Powered by real silicon tools and repeatable metrics, Rise AI combined with Rise tools deliver deterministic, verifiable, and directly implementable results.
From Structured Intent to Executable Specification
The Rise AI-Native system accepts system intent in any format, whether a spec document, an algorithm, existing HL design code, or legacy RTL. The specification does not need to be pin-level precise, what matters is clear hierarchy across purpose, interfaces, data types, and behavioral intent. Users can start with a minimal specification and add detail incrementally.
From this input, the Rise AI-Native system generates an executable specification in C++, SystemC, or SystemVerilog, the single source of truth for the hardware, software and system flow. Rise has also developed domain expertise tailored for key industry verticals including compute, AI acceleration, image processing, and signal processing, significantly accelerating design generation for these workloads.
The Rise High-Level Platform is equally powerful when the executable specification is written directly by the designer. Whether the input comes from the AI-Native system, is written by hand, or is a combination of both, the Rise synthesis and verification toolchain delivers the same deterministic, correlated results.
The Closed Loop
Tool Truth Drives Every Decision
LLMs excel at high-level languages, specifications, and architectural intent, but they are not built for RTL. High-quality RTL training data is extremely limited, LLMs cannot reason about timing, area, power, latency, or throughput, and their outputs are inherently non-deterministic in a domain where silicon requires determinism. Attempting to generate RTL directly from an LLM introduces hallucination risk at exactly the point where errors are most costly. Rise solves this by having AI operate at the high-level abstraction where LLMs actually work well, then using the deterministic Rise toolchain to produce RTL, with a closed loop that grounds every decision in real tool results.
At the heart of the Rise Agentic system is the Propose, Evaluate, Decide loop, a tool-gated closed loop where every decision is grounded in real synthesis and verification results.
Propose
The Rise Agentic AI system generates various high-level design candidates grounded in the Rise knowledge base. The Rise AI system is completely LLM model agnostic and the model can be upgraded at any time independently of the Skills and Knowledge base
Evaluate
The AI system then uses the AI-Native Rise High-Level Platform where it compiles and elaborates each candidate, runs synthesis and verification, identifies deterministic failures, and measures real PPA and QoR. All Rise tools are AI-Native where all metrics are visible and tools can course correct the models.
Decide
This Decide phase is the last in the closed loop system that uses only Tool Truth + Metrics to decide. The system compares candidates against user-defined constraints and targets, selects the best option, and generates the next refinement step. This guarantees that decisions are made only using the deterministic data of proven EDA hardware design and verification tools.
This architecture is why Rise delivers results that are deterministic, verifiable, and directly implementable. Every output traces back to real tool measurements.
This is what separates Rise from every other AI approach in chip design, not better prompting or more RTL training data, but a fundamentally different architecture that aligns with how LLMs actually work and what silicon actually requires.
The Knowledge Base
Agents That Understand Hardware, Not Just Code
What makes the Rise AI-Native system uniquely capable is the curated hardware knowledge base it is grounded in. The Rise knowledge base is built from multi-level coding standards and IP libraries, QoR understanding, domain-specialized IP, multi-level design and verification processes, and Rise documentation and libraries. This grounds the AI and gives the system access to everything an experienced hardware engineer needs to make informed design decisions.
Rise is model agnostic, supporting any modern LLM, whether frontier foundation models or internal enterprise models, and as new and more capable models emerge they can be integrated easily into the Rise AI-Native flow.
AI Orchestration
A Coordinated, Interactive AI System
The Rise AI-Native Designer coordinates the full workflow across multiple layers working in concert. At the top level, the system manages the end-to-end flow across module and testbench generation, simulation and verification, and design space exploration. Independent modules are architected, built, and synthesized concurrently, so work that can proceed in parallel does. When synthesis or simulation results come back, the system applies targeted fixes scoped only to what failed and re-runs, without restarting the full pipeline. Every result is measured, not estimated.
Everything that comes out is transparent and editable, giving users full visibility into every step of the process. Users can inspect, modify, and build on the outputs at any point, and reproducible configurations and scripts accompany every result so teams can verify and repeat the process with confidence.
Architected for Flexibility
Designed to Fit Into Your Environment and Grow With Your Needs
Rise Agentic AI is designed to fit into existing engineering environments without disruption. The agent loop is framework-agnostic, integrating across common front-ends and running the same way whether deployed on GitHub, Cursor, or cloud infrastructure. There is no lock-in to a specific toolchain front-end or workflow
Rise is model agnostic, supporting any modern LLM, whether frontier foundation models or internal enterprise models, and as new and more capable models emerge they can be integrated easily into the Rise Agentic flow. In addition, teams can extend the Rise knowledge base with their own IP, coding standards, and domain-specific content, giving the Agents access to institutional knowledge that is unique to their organization and design environment.
AI-Guided Design Space Exploration
Find the Best Architecture and Know it is Implementable
Every hardware design contains architectural decisions that profoundly affect the final product’s power, performance, and area. Most teams make these decisions early with limited data, and live with the consequences through tapeout. Evaluating architectural alternatives at RTL can take hours if not days per iteration, making meaningful exploration impractical on real project schedules. Rise changes this by running the closed loop at the high level where synthesis completes in seconds, enabling teams to generate and evaluate tens to hundreds of design candidates with correlated PPA feedback before committing to an architecture.
The Rise Optimization Agent uses algorithmic parameter search and learned predictions to converge toward an optimal Pareto front across any combination of metrics including pipeline depth, memory hierarchy, parallelism, area, latency, power, and timing closure. Rather than manual parameter sweeps, the system runs targeted trials and refines based on user-set goals and constraints, with full analysis, debug, and compare visibility at every step.
The flow is designed to be extended with higher fidelity estimation as needed. In one example, Rise integrated with hls4ml to take ML models directly from Keras, TensorFlow, or PyTorch through the full synthesis and verification loop, with the Optimization Agent managing Pareto front analysis across the parameter space. In another, Rise integrated OpenROAD as a plug-in PnR estimation phase, adding physical implementation correlation to the exploration loop and giving teams confidence that the architectures they are selecting will close in silicon.
See a 5-Minute Demo Using a Sobel Edge Detection Algorithm
Watch how the Rise Agentic system take a structured design specification, generate a synthesizable executable specification, and drive it through the closed loop to produce validated, production-quality RTL, transparently and with full visibility at every step.