AI-ACCELERATED · OUTCOME-PRICED · U.S.-BASED

Specialist systems depth where the product needs it. Commercial risk tied to proof.

Augment your team with one cross-stack U.S. delivery boundary that connects systems architecture and algorithms; MATLAB/Simulink and Python/NumPy reference models; FPGA and DSP/SDR implementation; firmware, emulation and validation. Your team retains product authority while gaining a measurable outcome, reusable evidence and operating knowledge.

SYSTEMS DESIGN FIRST

Start with the system, not a favorite platform.

Turn the idea into measurable behavior, profile representative workloads and candidate platforms, then partition by evidence. FPGA, custom ASIC, embedded compute or cloud is a design decision, not a predetermined answer.

SYSTEMS DESIGN / ALGORITHMS / PLATFORM ARCHITECTURE

Idea to product through executable behavior, measured partitions and traceable implementation contracts.

Modeling & analysis
MATLAB · Simulink · Python · NumPy · SciPy · executable golden models · floating/fixed-point correlation · workload profiling · latency, precision, bandwidth, memory and SWaP budgets
System architecture
ConOps · requirements and traceability · SysML/UML where useful · platform trade studies · FPGA/custom ASIC/CPU/GPU/ DSP/NPU/firmware/cloud partitioning · interface and state contracts · failure and recovery analysis
Outcome
A versioned reference behavior, defensible platform allocation, implementation-ready contracts and an acceptance matrix carried through target correlation.
01 · FPGA / RTL SYSTEMS

Architecture through target acceptance.

For streaming, memory, control and DSP subsystems where the real risk spans the model, RTL, firmware, host and board.

Risks owned

  • Architecture, partition and data movement
  • Precision, latency, memory and interface budgets
  • CDC/RDC, timing, power and resource closure

Delivery system

  • Reference-model correlation and RTL/HLS trade study
  • Reviewed microarchitecture and interface contracts
  • Unit, subsystem and target-hardware progression

Artifacts

Architecture decision record, dataflow and budget models, RTL/HLS baseline, constraints, register/interface collateral, regressions and release manifest.

Acceptance evidence

Numerical and protocol correlation, static-analysis disposition, simulation coverage, implemented timing/resources/power, sustained-rate behavior and representative end-to-end target tests.

02 · EMBEDDED FIRMWARE

Software that can move left, and survive hardware.

Boot, BSP, HAL, drivers, RTOS/Linux, host control and recovery designed as part of the hardware system.

Risks owned

  • Boot and hardware/software contract ambiguity
  • Concurrency, interrupts, memory and recovery
  • Platform divergence and irreproducible images

Delivery system

  • Host-native and virtual tests before RTL is ready
  • Emulator/FPGA/board reuse with stable scenarios
  • CI, trace, update/rollback and field diagnostics

Artifacts

Boot and state models, register contract, BSP/HAL/drivers, RTOS/Linux integration, test harnesses, reproducible image recipe, compatibility matrix and diagnostics.

Acceptance evidence

Repeatable boot, hardware-facing tests, fault/recovery behavior, emulator-to-board correlation, workload stability, image provenance and a controlled transition into post-silicon execution.

03 · EMULATION / PROTOTYPING

A platform is productive only when teams can use it.

Model releases, transactors, virtual/hybrid/ICE environments, firmware, workloads, regressions and operations treated as a shared engineering product.

Risks owned

  • Unrepeatable builds and fragile model releases
  • Missing peripherals, memory behavior or stimulus
  • Firmware blocked by access, debug or throughput

Delivery system

  • Use-case and KPI definition before platform work
  • Transactor/model integration and software enablement
  • Scheduling, utilization, triage and release operations

Artifacts

Release pipeline, synthesizable partition, transactors and models, firmware workloads, regressions, debug recipes, dashboards, compatibility manifest and user runbooks.

Acceptance evidence

Repeatable model releases, representative firmware/workload execution, debug turnaround, regression stability, performance/utilization measures and demonstrated adoption by intended users.

04 · VERIFICATION / VALIDATION

Evidence converges across abstraction levels.

Requirements-derived planning and reusable scenarios spanning static analysis, formal, simulation, emulation, FPGA, HIL and post-silicon.

Risks owned

  • Coverage without requirement closure
  • Scenarios that cannot move between platforms
  • Failures stranded between RTL, firmware and system

Delivery system

  • Assertions, formal, unit and differential tests
  • UVM/portable scenarios and fault injection
  • Cross-layer triage, waiver governance and reuse

Artifacts

Verification plan and matrix, assertion/test libraries, reference scoreboards, platform adapters, coverage model, regression taxonomy, failure dispositions and waivers.

Acceptance evidence

Requirement coverage, model/RTL/hardware correlation, corner and fault behavior, reproducible regression results, known-risk disposition and product-level scenario acceptance.

SYSTEMS DESIGN / ALGORITHMS / PLATFORM ARCHITECTURE

Idea to product. Partition after profiling.

Executable reference behavior and target-platform data drive the FPGA, custom ASIC, CPU, DSP, GPU, NPU, firmware, host and cloud boundary. Implementation begins only after the interfaces, budgets, failure behavior and acceptance evidence are explicit.

Risks owned

  • Unmeasured partition assumptions
  • Numerical drift and hidden data-movement cost
  • System diagrams disconnected from implementation

Delivery system

  • Representative workloads and platform profiles
  • Scored trade study with explicit margin
  • SysML/UML views only where they reduce risk

Artifacts

Executable model, dataset/workload corpus, precision and latency budgets, platform profiles, allocation scorecard, system/interface views and implementation backlog.

Acceptance evidence

Agreed numerical envelope, profiled platform fit, traceable allocation, co-simulation results, target correlation and end-to-end demonstration against the product scenario.

06 · DSP / SOFTWARE-DEFINED RADIO

Deterministic signal chains. Adaptive operating intelligence.

Deep DSP and FPGA lineage applied to SDR, base-station, radar, navigation, drone and defense-oriented platforms, from executable waveform behavior through deployable hardware.

Risks owned

  • Numerical drift across model, fixed point and FPGA
  • Throughput, buffering and RF-control interactions
  • Adaptive functions without bounded failure behavior

AI at the right boundary

  • Search-assisted partition and parameter exploration
  • Signal, interference and anomaly classification around a deterministic core
  • Shadow → advise → bounded control, with confidence gates, watchdogs and deterministic fallback
  • Critique-assisted test generation, leakage review and failure clustering

Deep DSP lineage

Multi-DSP scheduling research; Viterbi, FIR, DSL modem and wireless test foundations; Simulink/MATLAB-to-hardware work; xStellar base-station algorithm ports and cloud FPGA; Ectron navigation/SDR; RFSoC, radar, LiDAR and vision datapaths.

Acceptance evidence

Golden-vector correlation, quantization envelope, EVM/BER where applicable, channel and interference replay, sustained-rate behavior, latency and resource closure, hardware-in-loop execution and recorded fallback behavior.

Which subsystem or evidence gate is putting the program at risk?

Frame the milestone

U.S.-based, U.S.-citizen owned and operated; engineering work is performed by U.S. citizens. Defense-oriented capabilities are offered subject to applicable export-control, data-handling, spectrum and customer security requirements. No clearance, conformance, certification or controlled-environment claim is implied.