Since 2011, Magic Lens has helped teams turn difficult engineering into working hardware. From algorithms and ASICs to FPGA, firmware and full-system bring-up. We connect the whole stack.
Engineering hardware and systems since 2011.
Experience across programs involving these organizations.
Planet Labs
xStellar
Aeva
Compound Eye
Picarro
General Radar
Toshiba
Micron
Rambus
Cornelis Networks
Advantest
PDF Solutions
Ectron
Many startupsSelected customers and organizations represented in prior consulting and product-engineering experience. Meta experience: Accenture consulting supporting Meta. Company marks identify experience; no endorsement is implied.
When an algorithm meets hardware, or firmware meets first silicon, the hardest problems live at the interfaces. Magic Lens brings the depth to solve each layer, and the systems judgment to make them work together.
Turn MATLAB, Simulink and Python reference models into implementable architectures. Resolve precision, throughput, latency and hardware/software partitioning before they become expensive surprises.
Connect microarchitecture, RTL design and IP integration with verification planning, simulation, assertions and regression. Exercise the interfaces and corner cases that determine system behavior.
Bring up executable platforms for firmware, operating systems and real workloads. Connect model builds, transactors, partitioning and debug into a repeatable pre-silicon environment.
Translate signal chains and compute bottlenecks into streaming RTL/HLS, with explicit timing, memory and interface budgets. Carry the design through implementation and target validation.
Build the low-level software that turns hardware into a usable platform: boot flows, drivers, RTOS and Linux integration, processor control and host interfaces.
Connect board, FPGA, ASIC, firmware and host behavior in the lab. Isolate failures, correlate against the model, and validate real workloads across pre- and post-silicon systems.
Start where your program needs us. We can own a focused workstream or connect the path from reference behavior to validated hardware.
Create a reference you can trust. Quantify numerical accuracy, compute load and latency; decide what belongs in software, FPGA or ASIC before committing the architecture.
Executable reference models, precision budgets and a measured hardware/software partition.
Selected work from our consulting and product-engineering experience. Different applications. The same ability to connect algorithms, hardware and software.
SPACE / PAYLOAD SYSTEMSMicrosatellite FPGA work connecting camera acquisition, processor data movement, encryption, communications and board control on a Stratix 10 platform.
VISION / HARDWARE ACCELERATIONReference modeling, RTL/HLS, PCIe/AXI/DMA, host C and RISC-V/Linux brought together in an end-to-end vision acceleration system.
PRE-SILICON / PLATFORM ENGINEERINGPlatform architecture, prototyping workflows, board delivery and customer evaluations connected through hands-on technical enablement.
Illustrations are conceptual and do not depict customer hardware. Prior experience is not a guarantee of future results.
Semiconductors ↗ Space ↗ Medical imaging ↗ Robotics ↗ Radar & communications ↗ Datacenter & storage
Deep algorithmic experience.
Decades of DSP design and implementation.
Magic Lens brings deep experience across DSP algorithms, signal-chain design and real-time implementation, grounded in graduate-level signal-processing work with DSP pioneers. Our experience spans FIR filtering, Viterbi decoding, modem and baseband processing, synchronization, multi-DSP scheduling, radar, imaging and software-defined radio.
We carry algorithms from MATLAB, Simulink and Python reference models through fixed-point analysis, hardware/software partitioning and FPGA, ASIC or embedded implementation. Numerical accuracy, throughput, latency and resource budgets stay connected through verification and validation on real hardware.
Need an expert inside your team? A partner for a difficult subsystem? Ownership of a defined deliverable? We shape the engagement around how you need to work.
Experienced engineers working inside your architecture, tools and review cadence. Add targeted capacity where it matters most.
A focused partner for modeling, ASIC verification, FPGA implementation, emulation or bring-up, with clear ownership and close collaboration.
Agree on the deliverable, acceptance test and price before implementation. For eligible milestones, payment is due when the completed work passes the agreed test.
Clear technical ownership. Reviewable engineering. Documentation and knowledge transfer. Your team stays in control.
AI-assisted workflows help us move faster through modeling, implementation, test development and documentation. We use that efficiency to offer flexible commercial terms, including outcome-based pricing for well-defined work.
For a startup or a lean engineering team, that means a known milestone price and payment tied to an accepted result. Start with one critical deliverable, demonstrate that it works, then decide what comes next.
Discuss an outcome-based project ↗Agree on scope, dependencies, price and a measurable acceptance test.
AI accelerates the workflow. Engineers review the design and verify the result with reproducible tests.
For agreed outcome-based milestones, we bill when the work meets the written acceptance criteria. If it does not pass, that milestone payment is not due.
Outcome-based terms are agreed per project. Discovery or definition work may be a separate paid engagement. Customer dependencies, scope changes and acceptance responsibilities are established up front. Staff augmentation, time-and-materials and fixed-scope options remain available.
A new architecture. A verification gap. First silicon.
A system that almost works. Start there.