News & Insights

Re-Architecting Edge AI at the Sensor Boundary with FPAA

Re-Architecting Edge AI at the Sensor Boundary with FPAA

Re-architect Edge AI at the sensor boundary. See how FPAA analog feature extraction reduces over-digitization and improves system-level efficiency.

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Building a High-Sensitivity EKG with the Okika FPAA Sing1

Building a High-Sensitivity EKG with the Okika FPAA Sing1

Explore a hands-on experiment using the Okika FPAA Quad4 to unlock cleaner EKG signals and precise Wheatstone measurements through reconfigurable analog design.

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Building Energy-Efficient Neuromorphic Systems with FPAAs

Building Energy-Efficient Neuromorphic Systems with FPAAs

Insights from Dr. Jennifer Hasler on neuromorphic hardware, analog computing, and how FPAA technology could dramatically reduce AI power consumption.

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Common First-Time FPAA Design Mistakes and How to Avoid Them

Common First-Time FPAA Design Mistakes and How to Avoid Them

Engineers moving from digital FPGAs to FPAAs often run into unexpected analog behavior, including gain issues, clipping, noise, and imperfect filtering. Early mistakes usually come from applying digital assumptions, underestimating headroom, and relying too heavily on simulation. This article explains common first-project pitfalls and how disciplined gain planning, incremental testing, and measurement-driven iteration lead to stable FPAA systems.

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Integrating FPAA Designs with MCU and FPGA Systems

Integrating FPAA Designs with MCU and FPGA Systems

Most real systems combine analog and digital components, with FPAAs often working alongside MCUs and FPGAs. System stability depends on clearly defining what each device is responsible for: signal conditioning in analog, control in MCUs, and high-speed processing in FPGAs. Many integration issues arise from blurred boundaries, especially in timing, power, and signal interfaces.

Successful designs treat the FPAA–MCU–FPGA split as an architectural decision, not just a connectivity problem. Careful attention to interfacing, grounding, and latency ensures predictable behavior and avoids complex debugging later.

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FPAA vs. Analog Front-End ICs: When to Choose a Reconfigurable Solution

FPAA vs. Analog Front-End ICs: When to Choose a Reconfigurable Solution

Discover how FPAA technology provides greater flexibility, faster prototyping, and longer system life compared to traditional Analog Front-End ICs, and learn when a reconfigurable approach is the smarter choice.

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FPAA Applications in Sonar: Low-Power, High-Fidelity Signal Processing for Complex Environments

FPAA Applications in Sonar: Low-Power, High-Fidelity Signal Processing for Complex Environments

Field Programmable Analog Arrays bring adaptive, low-power analog processing to modern sonar systems. This article explores how FPAAs improve multi-spectral performance, reduce latency, and simplify analog front ends for underwater robotics, inspection devices, and advanced sensing applications.

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SoC FPAAs: Unlocking Hidden Knobs in Software-Defined Systems

SoC FPAAs: Unlocking Hidden Knobs in Software-Defined Systems

Software defined systems reshaped digital design, but analog has remained fixed. SoC FPAAs change this by making filters, amplifiers, and sensor front ends fully programmable. They reveal new hidden knobs for adaptive payloads, reconfigurable instrumentation, and low power mixed signal computing. This is the next wave of software defined engineering.

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How FPAAs Can Save Power in Modern Analog Systems

How FPAAs Can Save Power in Modern Analog Systems

FPAAs improve power efficiency by replacing many always-on analog components with a single device that activates only the circuits a system needs. By reducing idle draw, limiting unnecessary conversions, and streamlining signal paths, FPAAs help engineers build lighter, longer-lasting, and more efficient systems for aerospace, robotics, medical devices, and industrial IoT.

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