AI Accelerator Compiler
Turn AI Models into Cycle-Accurate AI Accelerator Architecture - in Hours, not Months
Transform trained AI models and workloads into custom accelerator architectures, optimizing compute, memory, and dataflow to meet real silicon constraints across edge to data center deployments.
Tired of generic AI compilers that fall short for custom hardware?
Connexus' AI Accelerator Compiler is purpose-built for AI chip designers, delivering cycle-accurate simulations, detailed PPA predictions, and production-ready outputs in days, not months. Input your AI model and performance targets. Get an optimized architecture, detailed workload analytics and production‑ready HDL in one push. Whether you're an architect exploring trade-offs, an engineer optimizing RTL, or a founder proving your chip concept, this compiler turns weeks of manual analysis and coding into hours of actionable results – helping you build faster, smarter, and more efficient AI silicon.
How It Works
Simple Inputs, Powerful Outputs
Our compiler maps architectural choices & CNN model into performance insight & hardware-ready deliverables. Instantly expose real compute and memory bound bottlenecks with clear guidance on tuning MAC count, precision and bandwidth.
Easy Inputs
- Your AI Model
Upload your CNN model in common formats: Caffe, TFLite, ONNX, PyTorch (e.g., AlexNet, ResNet, and other modern networks) – no manual conversions needed.
- Design Targets
Set the desired requirements for precision (INT8/FP16/mixed for accuracy-efficiency balance), MAC array size, clock frequency, and memory bandwidth targets.
Comprehensive Outputs
- Architecture Intelligence Report
PPPA, FPS, Bandwidth Efficiency, GFLOPS, GFLOPS/Watt, plus a layer-by-layer network summary of convolutions, elementwise ops & pooling with cycle counts (remap in/out, MAC, data conv), memory reads (weights/data), and per-layer power estimates.
- AI Accelerator IP
Synthesizable HDL, gate-level netlists, testbenches, UVM verification environments, SDC, power/timing analysis reports and integration guides for seamless SoC embedding.
Common Challenges Teams Face with AI Compilers – and What We Do Differently
Most AI accelerator compilers focus on generating RTL.
Our customers needed something more fundamental: clear architectural insight before RTL exists.
So we built an AI Accelerator Compiler designed for architecture understanding, fast iteration, and silicon-ready decisions—not just code generation.
I can't see where my design is leaving performance on the table.
Traditional tools report a single throughput or TOPS number, hiding where performance actually stalls.
Our Key Advantage
Our compiler exposes layer-by-layer cycles, memory traffic, and power, making compute and memory bottlenecks immediately visible.
Every design change means rebuilding all my spreadsheets.
Many teams still depend on fragile spreadsheets and scripts that break when precision, MAC count, or models change.
Our Key Advantage
Our compiler automatically recomputes all performance, bandwidth, and power metrics when inputs change enabling fast, consistent architectural exploration without manual recalculation.
We lose months aligning models, hardware, and verification.
Disconnected workflows between ML, architecture, RTL, and DV teams cause costly rework.
Our Key Advantage
Our compiler uses the model-driven flow generating hardware outputs and verification artifacts from the same reference behaviour, keeping teams aligned from early exploration through RTL and validation.
RTL readiness still takes too long.
Many AI compilers stop at functional mapping or high-level hardware descriptions, leaving teams with RTL that is incomplete.
Our Key Advantage
Our direct HDL, netlist, and verification kit generation saves weeks of manual translation, ensuring a shorter, clearer path from model to architecture to RTL.
PPA surprises show up too late.
Many tools ignore real bandwidth and memory constraints until after synthesis.
Our Key Advantage
Physical-aware insights before committing to silicon.
Get Started
Upload your model, configure your accelerator, and see the difference. No hardware needed – just results.
See how much visibility you're missing—and why teams say they make better decisions after just one run.