What is AI hardware engineering?

The electronics R&D that gives an AI workload a home on a device: architecture, compute selection, memory, interfaces, power, thermal design and board bring-up, engineered around the workload's measured needs and the years the device must serve.

Do you design custom boards or use off-the-shelf modules?

Both, decided by the evidence. An off-the-shelf compute module on a custom carrier board is often the fastest route to a pilot; a fully custom board can win on unit cost, size or lifecycle at volume. The assessment sets out which applies and what it costs.

How do you choose between NPU, GPU and FPGA?

From the workload backward: model size and type, latency, power and thermal budget, unit cost at fleet scale, and how fixed or changeable the workload is. NPUs suit efficient fixed-model inference, GPUs heavier or evolving models, FPGAs deterministic pipelines with hard latency limits.

Can new hardware be added to equipment that is decades old?

Often, if the equipment exposes usable interfaces or signals and can supply or accommodate the added power and space. The hardware interface assessment in an edge AI retrofit establishes this before any board is designed.

AI Hardware & Electronics R&D Engineering

What AI hardware engineering covers

The compute platform decides what a device can ever do. Choosing it well needs the workload, the enclosure, the power available and the years of service life on the table at the same time. Our electronics R&D covers architecture definition through prototype validation; PCB layout, signal integrity and manufacturing readiness are described under high-speed PCB and product engineering.

Architecture definition

Architecture starts from the signal path: what is sensed, where it is processed, what leaves the device, and what the device must do when something goes wrong. From that come the compute class, memory budget, interface list and power architecture, written down before parts are chosen so the trade-offs are explicit.

Compute selection: MCU, MPU, SoC, FPGA, NPU, GPU

Each class is selected for a reason that can be measured.

MCU: control loops, sensor interfaces and small models where power and cost dominate.
MPU and SoC: Embedded Linux platforms with connectivity, storage and moderate inference.
NPU and AI accelerators: efficient on-device inference for fixed vision and signal models.
GPU modules: heavier or evolving models and high-throughput vision.
FPGA: deterministic pipelines, custom interfaces and hardware acceleration with hard latency limits.

Selection is checked against the workload's measured throughput, latency headroom and power draw, against enclosure thermal limits, and against component availability for the fleet's expected service life. We do not design around a preferred vendor; we select around the system that must succeed.

Memory architecture and interfaces

Model size, runtime footprint and working buffers set the memory budget. Interfaces are chosen for the sensors and for the existing system the retrofit connects to: camera inputs such as MIPI CSI, industrial buses such as CAN, RS-485 and Ethernet, and standard peripherals such as SPI, I²C, UART and USB. High-speed memory and storage interfaces are designed with signal integrity in mind from the outset.

Sensor interfaces

Retrofits often begin with a sensor the existing system never had: a camera on an analogue line, vibration and current sensors on a PLC-driven machine, a microphone array on a collar. Acquisition, conditioning and sampling are engineered for signal quality in the real electromagnetic environment, because a model cannot recover information the front end lost.

Power and thermal engineering

Continuous and peak power are budgeted against what the installed system can supply, and the thermal design is validated by soak testing inside the real enclosure rather than on an open bench. A module that throttles at operating temperature does not meet its latency target, whatever the datasheet says.

Board bring-up and prototype validation

Bring-up covers power sequencing, boot, clocks, memory and interface checks, then firmware bring-up on the new board, described under embedded firmware and edge platforms. Prototype validation measures the six constraints, latency, power, memory, thermals, lifecycle and cost, before a design is released for pilot units.

FPGA and hardware acceleration

Where a workload is fixed and deterministic, FPGA acceleration can deliver latency and power figures that general-purpose compute cannot. We evaluate FPGA acceleration and AI-accelerator integration as options within a hardware design, with the trade-off in development effort, toolchain dependence and lifecycle stated plainly, and we recommend it only where the evidence supports it.

What we do not do

RETONAI does not manufacture silicon or operate a fabrication line. We design with commercially available processors, accelerators and FPGAs, and we name platforms only as targets we build on, never as partnerships or certifications.

Related capabilities

Edge AI retrofit · Embedded AI engineering · Embedded firmware and edge platforms · Embedded and edge cybersecurity · High-speed PCB and product engineering · All capabilities · How the edge stack works

AI Hardware & Electronics Engineering