quartus

Tool List Professional, evidence-based deep dive into Altera **Quartus Prime Pro 26.1** and **FPGA AI Suite 2026.1.1** scored on **30 criteria × 10-point scale** from an *agentic-AI* (LLM-driven autonomous) perspective. **Reference date:** 2026-05-21 · **Subject:** Quartus Prime Pro 26.1 (2026-04-06) + FPGA AI Suite 2026.1 (released 2026-05-01 **Author:** Claude Op4.7 --- ;DR > **Quartus 2026.05 agentic-readiness score: 4.07 / 10** (equal-weighted) — **3.98 / 10** (agentic| Axis | Score | Note | |---|---|---| | A. & Automation | 22 / 50 | Tcl scripting depth is 9/10, Python SDK is 2/10 | | B. Observability & State | 20 / 50 | Lack streaming/JSON report | | C. Determinism & Reibility | 29 / 50 | Good seed control/incremental compile | | D. AI-Native Features | 1850 | AI Suite is strongAI Copilot is missing E. Ecosystem & Standards | 24 / 50 |, weak Docker/license elasticity | | F. Agentic Aut Safety | 10 / 50 | **MCP 10, async 2/10** — weakest area | GA market position is after. Compared to ASICSynopsys.ai / Cadence Cerebrus / Siemens Apr there is a 2x gap.** --- ## What's in this repo - [`report/Quartus_Agentic_AI_Readiness_Report_2026-05.md`](report/Quartus_Agentic_AI_Readiness_Report_2026-05.md) — **Part I: Main Evaluation Report** (≈,500 words, 9 sections, 30 criteria scoring table, 3-year improvement roadmap - Question: *Can existing surface be used by agent? **4.07 /** - [`report/Talking_Points_RTL_Verification_Argument.md`](report/Talking_Points_RTL_Verification_Argument.md) — **"Why RTL Verification affects customers" meeting talking points** - 30-second definition → 4-step logical chain → 3 counterarguments - 4 scenarios (synthesis miscompile / STA optimism / Hyper-Aware retiming / IP RTL bug) - 1-minute elevator pitch / tone advice / vocabulary alignment - [`report/Quartus_vs_Suite_Prec.md`](report/Quartus_vs_AI_Srecise_Comparison.md) — **Quartus vs AI comparison** (≈ 4,500 words) - One Quartus is like `gcc` for FPGA, AI TVM-like compiler for AI IP - comparison table / actual CLI workflow (4 stages) - **DLA (instruction-fetched) vs Spatial (graph-to-RTL, 2026.1.1 new)** compiler arche - 5 license combination scenarios / device overlap & gap 5 frequently asked questions - [`report/Part_Agentic_Native_Architecture.md`](report/Part_Agentic_Native.md) — **: Agentic-Native Architecture** (≈ 5,500 - Question: * agent is 1st* → 40 criteria (+G/+H axes), **3.23 / 10** criteria) - ** Verification 7 capabilities** (V1–V7): spec/TB/RTL generative testbench, coverage failure triage, formal orchestration, mutation gate-in-the-loop (FPGA asymmetric strength)** - 15 additional architectural shifts (S1–S15) 5 asymmetric strengths of FPGA over ASIC - 3-year integrated roadmap → **8.75 / 10** 5 years - 5 PoCs that created within 1 weekTap Debug Agent / coc gen / MCP v1 closed-loop / HW-in- [`reports/quartus_eda_agentic_ai_analysis_2026-05-21.md`](reports/quartus_eda_agentic_ai_analysis_2026-05-21.md) — Companion analysis (traditional 7.45 + 3.25 split → 6.05/- [`scripts/`]( — Reproducible smoke (Quartus Tcl + bash) - [`smoke_designounter/`](sms/counter/) —ilog counter + SDC - [`mcp-probe/`](mcp-probe/) — Analyzed and verified community Quartus MCP servers (source inspection materials) - `quartus_mcp_server/` — `irumvag/quartus_mcp_server` (Quartus II 13.1, Windows, 38 tools, 2026-05-09 push) - `fpga-mcp-servers/` —be/fpga-mcp-servers` (Quart24.1 Std + DE10-Nano, async machine, 2026-04-14 push) --- 1. **Primary-source verification** — altera.com / docs.altera.com / Altera Newsroom / Altera Community blog direct fetch 2. **3-agent parallel research** — Quartus capabilities · competitive EDA agentic landscape · Quartus AI ecosystem 3. **Community MCP server source inspection** — clone → grep → structure analysis ( automation surface verification by agent. **30 criteria definition → weighted aggregation →/P1/P2 improvementEach score cell has evaluation citation URL. Due to macOS arm64 environment constraints, native Quartus installation was not performed (Quartus is Windows86_64 only; GB installation). Detailed constraints report §1.2 / §9. --- ## findings ### Strengths **Tcl scripting depth (9/10)** — deepest and most mature automation surface. fully queryable via Tcl. - **Headless CLI completeness (8/10)** — `quartus_sh / _map / _fit / _asm / _sta / _powdrc / _pgm / _dse` all mature. - **Standards compliance (8/10)** — SystemVerilog 2017, VHDL 2008/2019, SDC. - **FPGA AI Suite (8/10)** — Spatial AI IP (MLP public beta), Graph-to-RTL, 500K ALMs scaling. *Deploy AI on FPGA* tool. (Separate category from *agentic Quartus*.) Compile time wins in 1** — Agilex 7 −9% runtime0.6% Fmax, Agilex 5.5% runtime / +3.3% F### Critical gaps - MCP server absence (10)** — F1/Synopsysens also lack,two community MCPs* already active, indicating Quartusfertile ground*. - **GenAI Copilot absence/10)** — D Synopsys.ai Copilot, Cadence Chip GPT GA. Altera's not announced. - **REST/gRPC API absence (1/10)** — A4. - **Cost/runtime estimator absence (1/10)** — F3. - **OpenTelemetry hooks absence (1/10)** — B4. - **Native Python SDK2/10)**3. 26's SignalTap Python API-only. - ** reports absence (2/ A5. All reports are `.rpt` HTML. - ** elasticity absence (2/ — E4., AI Suite 10 cap. - ** task semantics absence (2/ — F2. abbbe MCP uses `fcntl` + JSON state. --- ## Improvement Roadmap (summaryP0 (6-12 months) — 5 items:** 1. `altera-quartus-mcp` (native MCP server) — F 0→8 2. `--json` flag CLI binary — A2 3. `pyquartus` typed Python3: 2→8 4. Async task semantics — F2/F5 5. Official Docker + GitHub cloud license bridge — E3/E4 **P1 (12-18 months)5 items:** - Copilot ("Q") · ML-assisted P prediction · Structured constraint OpenTelemetry · Cloud license **P2-24 months) — items:** - Multi protocol · Version diff API · `quartestimate` runtime predictorx Yosys backendplan`/`--apply` dry-run · Quart (cloud IDE) · Studio CLI export ed trajectory (Part 30 criteria):** | Score | |---|---| | Today (2026) | **4 10** After P0 (12 | **6.20 / 10** | P0+P1 ( | **7 / 10** | P0+P1+P2 (36 mo)8.30 / | Completing can reach **ys.ai / Cadence equivalent agentic 7.5**Extended trajectory (Part II, 40 criteria + architecture):** | Stage | Score (40 criteria) | |---|---| | Today (2026-05) | **3.23 / 10** | | After P0 (12 mo) | **5.00 / 10** | | After P0+P1+P2 (36 mo) | **7.50 /** | | **0+P1+P2+P3 ( | **8. 10** | 's key message: I improvements (MDK/JSON/async)defensive*. Real Agentic RTL Verification + Hardware-in-the-Loop-silicon closed Instead of catching upIC EDA, new *agentic-native* leveraging FPGA's (fast iteration cycle lab economics) in strategy. --- ## matters (Alpha OmegaASIC EDA has for PPA 1% to high tapeout FPGA has ambiguous ROI near-zero fitter **However, 2026's edge-AI / physical AI era requiresthousands of model — AI Suite 2026.1.1's Spatial AI IP (dedicated model) is a signal. Agentic automation value grows in FPGA as in ASICCompleting P0 items within 1 *defensive*. Achronix Speedsterix Quantum,GA startups bypass Quart NL→bitstream-first flow, Altera mid-range edge-A. --- ## License This report is for evaluation/research purposes. For: ``` Quartus Agentic-AI Readiness Analysis6-05). Generated 2026-21 with Claude Opus 4.7 multi-agenthttps://github.com/quasar17/quartus ``` --- ## Disclosure environment is macOS arm64, no native Quart - All scores are-based* and may be affected by non-public roadmap surprises WebFetch failed in some SPA, replaced with + community blog - All citations are in report § with URLs attached