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Edge AI Solutions   

Integral & Open Systems,Inc

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Integral & Open Systems (IOS) designs and delivers custom edge AI inference subsystems on Altera Agilex 5, Cyclone V, Cyclone 10, and MAX 10 device families, optimized for deterministic low-latency operation in SWaP-constrained environments. Our engagements span model porting and quantization, RTL accelerator design, AI Tensor Block utilization on Agilex, integrated Arm and Nios V processor configuration, and full embedded software bring-up — from architecture trades through silicon-in-the-loop verification and sustainment.

We support the full edge AI deployment spectrum: TinyML for power-constrained sensor nodes, mid-scale CNN inference for vision and signal classification, recurrent and transformer-lite models for time-series tasks, and hybrid CPU-FPGA pipelines for systems that need both flexibility and acceleration. Typical applications include industrial machine vision, medical imaging preprocessing, ISR sensor classification, autonomous platfor...

Integral & Open Systems (IOS) designs and delivers custom edge AI inference subsystems on Altera Agilex 5, Cyclone V, Cyclone 10, and MAX 10 device families, optimized for deterministic low-latency operation in SWaP-constrained environments. Our engagements span model porting and quantization, RTL accelerator design, AI Tensor Block utilization on Agilex, integrated Arm and Nios V processor configuration, and full embedded software bring-up — from architecture trades through silicon-in-the-loop verification and sustainment.

We support the full edge AI deployment spectrum: TinyML for power-constrained sensor nodes, mid-scale CNN inference for vision and signal classification, recurrent and transformer-lite models for time-series tasks, and hybrid CPU-FPGA pipelines for systems that need both flexibility and acceleration. Typical applications include industrial machine vision, medical imaging preprocessing, ISR sensor classification, autonomous platform perception, broadcast video analytics, and federated edge analytics for distributed sensor networks.

IOS pairs Edge AI engineering with our AI/ML modeling, cloud, and DevSecOps teams, so the same FPGA design that runs at the edge can be paired with a federated training pipeline, cloud back-end, and CI/CD release flow — reducing time-to-field for AI-driven products. We work natively in Quartus Prime Pro 26.1, Platform Designer, OpenVINO toolchains, and HLS flows. The company is a CMMC Level 2 self-certified, JCP-certified Small Disadvantaged Business with active DARPA Phase II execution and GSA MAS contract 47QTCA23D00CP for rapid federal contracting.

Key Features

  • TinyML to mid-scale inference (CNN, RNN, transformer-lite) on FPGA
  • Quantization-aware deployment (INT8, FP16, BF16) with accuracy preservation
  • AI Tensor Block utilization on Agilex devices
  • Arm and Nios V soft processor integration with Linux BSP and bare-metal drivers
  • OpenVINO model deployment pipeline integration
  • SystemVerilog / UVM verification harness with reference test vectors
  • Federated learning and DevSecOps release pipeline tie-ins available
  • CMMC L2, JCP-certified delivery for export-controlled programs
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Offering Brief

Offering Brief

Device Family Agilex™ 9 FPGA Direct RF-Series, Stratix® 10 TX FPGA, Agilex™ 7 FPGA I-Series, Stratix® 10 GX FPGA, Stratix® 10 DX FPGA, Agilex™ 5 FPGA E-Series, Agilex™ 3 FPGA C-Series, Agilex™ 7 FPGA F-Series, Stratix® 10 SX FPGA, Stratix® 10 AX FPGA
Offering Status Production
Demo No
Integrated Testbench No
Evaluation License No
Design Examples Available No
Compliance No
Latest Quartus Version Supported 26.1.0
Development Language C/C++, Verilog, VHDL
Prerequisites Customer-provided AI/ML model (PyTorch, TensorFlow, ONNX, or equivalent) or model specification, target Altera device family, performance envelope (latency / throughput / power), and representative dataset for quantization-accuracy validation. NDA in
Languages English
Target Audience Defense and aerospace prime contractors, federal sponsors (DoD, IC, DARPA, NGA), industrial automation builders, medical device manufacturers, broadcast video integrators, and edge-AI product teams requiring defense-grade engineering discipline on Al
Duration Engagement duration scoped per project; typical edge AI subsystem designs run 4–12 months from kickoff through silicon-validated delivery.
Hands On Lab False

AI/ML model porting, quantization, and FPGA mapping (INT8 / FP16 / BF16)

RTL or HLS accelerator development targeting Agilex AI Tensor Blocks or Cyclone DSP fabric

Arm and Nios V embedded software, Linux BSP, bare-metal drivers

SystemVerilog / UVM verification with reference test vectors

Board bring-up, signal integrity analysis, hardware debug (Signal Tap, System Console)

OpenVINO integration for model deployment

Host-side application integration and inference API

Optional add-ons: federated training pipeline, cloud back-end, DevSecOps release pipeline, secure boot integration

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