Sensing the Future: Unlocking the Potential of TinyML for Japan’s Aging Infrastructure

Japan has world-class disaster-proofing infrastructure, as demonstrated by the late-July earthquake in Kyushu. Known as Japan’s silicon island, Kyushu houses vendors like Taiwan Semiconductor Manufacturing Company (TSMC), Sony, Tokyo Electron, and Renesas, which saw minimal impact. Nevertheless, tragedy still struck at a local shopping mall when an explosion killed two store workers.

Despite the limited damage and casualties in Kyushu, not all of Japan is equally resilient, particularly in its rural areas. The Noto Peninsula, which was struck by a 7.6-magnitude quake in 2024, faced over 100 deaths and reconstruction delays due to its remoteness and aging-related manpower shortages. Furthermore, Japan’s aging infrastructure is another critical issue, with more than 60% of infrastructure like bridges and river facilities set to reach 50 years old in 7 years. The consequences are already apparent, with over 22,000 sinkholes recorded nationwide in the 2 years through Fiscal Year (FY) 2024.

Tiny Machine Learning (TinyML)—an ultra-low-power edge AI—offers a compelling solution for Japan. TinyML is a subfield of Machine Learning (ML) and embedded systems that runs on-device inference on resource-constrained hardware, particularly Microcontroller Units (MCUs), and operates for months or years on a single coin-cell battery. The local processing allows it to operate in offline or intermittent connectivity environments. Ultimately, the low-touch autonomy addresses Japan’s key challenges—manpower shortages and remote infrastructure monitoring.

The technology is not new to the market. In consumer devices, it powers always-on voice triggers like “Hey Siri” or “Hey Google” on smartphones. In the industrial and commercial segments, it enables predictive maintenance by analyzing vibration, acoustics, or thermal data directly on equipment to prevent failures. Combined with vision and motion sensors, it also enables people counting, agricultural anomaly detection, or gesture recognition using ultra-low-power image sensors and Inertial Measurement Units (IMUs).

When it comes to the civil infrastructure vertical, it addresses both humanitarian needs and substantial business potential. This is especially true since Japan has long-term disaster prevention and mitigation plans dedicated to natural disaster events like earthquakes and infrastructure overhaul—allocating 15 trillion yen (US$94 billion) for FY 2021 to FY2025 and over 20 trillion yen (US$125 billion) for FY 2026 to FY2030.

TinyML can be categorized into two types of systems based on hardware architecture and computing models:

• Conventional TinyML: Refers to the execution of ML models on standard, general-purpose MCUs using clock-driven instruction processing to analyze sensor data locally within tight power and memory constraints. Key vendors include STMicroelectronics and Silicon Labs, alongside other established market players.
• Neuromorphic TinyML: Refers to the execution of event-driven neural models on specialized, bio-inspired architectures that compute asynchronously only when sensor data change, enabling ultra-low power consumption at the microwatt level. Key vendors include BrainChip and SynSense, among others.

Both systems offer significant potential value across all phases of an earthquake event: before, during, and after impact.

Before and During an Earthquake—Instantaneous On-Device Detection and Local Safety

Traditional earthquake early warning systems rely on centralized networks transmitting seismic data back and forth to central servers, which create network latency. TinyML addresses these limitations through:

• Instant Detection & Automated Cut-Off: TinyML hardware paired with a Micro-Electro-Mechanical Systems (MEMS) accelerometer can continuously monitor P-wave vibration produced by earthquakes before the destructive S-waves arrive. The local processing capability reduces latency for warning systems and enables localized actions like cutting off gas lines to prevent post-quake fires or explosions, halting high-speed trains, and freezing industrial robotics.
• Off-Grid Resilience: TinyML hardware runs on battery cells and possesses on-device inference capability, so it remains operational even if the electrical grid or cell towers go down during the initial shock.

Post-Earthquake Infrastructure Checking—Structural Health Monitoring

Assessing whether high-rises, bridges, tunnels, railway lines, or manufacturing facilities are safe after an earthquake usually requires manual visual inspections, which can take days or weeks. TinyML provides several key advantages:

• Anomaly Detection & Sparsity-Driven Efficiency: TinyML hardware can sit embedded inside concrete pillars, bridge joints, or high-rise support beams. It can spot internal stress fractures, bolt looseness, or micro-cracks that are not visible to the naked eye by listening to acoustic emission patterns or monitoring structural resonance frequencies. Furthermore, it achieved energy efficiency by filtering out normal ambient vibration caused by wind or traffic and only computing and alerting when abnormal seismic deformation or structural frequency shifts occur.
• Building Safety Triage: After the earthquake, a mesh network of TinyML sensors across a skyscraper, enabled by long-range, low-power mesh protocols like LoRa mesh, can give emergency responders an instant “heat map” of structural damage levels, speeding up citywide recovery.

The TinyML hardware market (excluding consumer devices) has a favorable outlook, with chipset revenue expected to grow sixfold from 2025 to 2031. Nevertheless, smart buildings and smart cities together represent under 20% of the market in 2025, declining to nearly 11% by 2031. Meanwhile, automotive remains the major vertical throughout the period. The key hurdles to growth in the infrastructure segment include strict regulatory certification processes, zero tolerance for false alarms, and high labor and equipment costs for upgrading existing systems.

To unlock the high-value infrastructure opportunities, high precision and accuracy and device security are merely baseline requirements. A “stick-and-forget” solution and edge observability features will be the key value propositions to address the market hurdles. BrainChip’s AkidaTag reference platform—a hardware reference design and silicon blueprint—is a typical example of a “stick-and-forget” solution that minimizes Capital Expenditure (CAPEX) by allowing infrastructure retrofitting without invasive installation. Beyond the retrofitting approach, edge observability features also play a crucial role—it not only provides an audit trail for the regulators but also addresses the trust deficit driven by black-box skepticism. And this could be achieved by combining TinyML sensor nodes with a local edge gateway, allowing an efficient way to split the on-device inference and observability workloads.

Japan is just an example of how an aging population and infrastructure can leverage the value of TinyML. In the recent earthquake events in Venezuela and Colombia, the economic damage to the countries demonstrates how crucial a disaster-mitigation program is. Although TinyML is unable to prevent natural disasters, it can speed up the economic recovery of a city or even a country.