Physics-first embedded battery intelligence for handheld devices
Seer Labs is adapting its proven physics-first Edge AI platform to deliver on-device battery intelligence for handheld devices. Using existing voltage, current, temperature, and load signals, the solution estimates battery state, predicts remaining runtime, detects degradation or anomalies, and supports adaptive power management—without cloud dependency or additional sensing hardware.
Overview
Seer Labs is proposing an embedded battery-intelligence solution for compact handheld devices. Built on the company's physics-first Edge AI platform—already used in demanding real-time control applications—the solution runs entirely on the device, using signals that are normally already available. It is designed to let product manufacturers add accurate battery state estimation, remaining-runtime prediction, degradation and anomaly detection, and adaptive power-management decisions without requiring a cloud connection or new sensing hardware.
The value proposition is practical: the approach can help extend usable runtime, give users more confidence through accurate run-time estimates, provide earlier warning of battery degradation or abnormal behavior, and reduce system cost by avoiding additional components and cloud services. Because the model is compact and device-resident, it also preserves user privacy and continues to work in disconnected or low-connectivity environments.
Technical specifications
How it works
Seer Labs takes a physics-first approach: the inference model embeds knowledge of the underlying physical behavior of the battery and device instead of relying on large, generic neural networks. This allows accurate state estimation and prediction from a small set of real measurements under tight memory and processing constraints.
Key features
- Uses existing measurements: voltage, current, temperature, and load history
- Builds a compact on-device model for battery state estimation, for example state of charge and state of health
- Predicts remaining runtime under current operating conditions
- Detects degradation trends and anomalies without cloud analysis
- Supports adaptive duty-cycle or power-management decisions in real time
- Optimized for embedded microcontrollers and DSPs with low memory, compute, and power overhead
- No cloud dependency and, where possible, no additional sensing hardware
Target applications
The solution applies to battery-powered handheld devices where embedded intelligence can improve runtime, safety, and maintenance planning.
Technology readiness level
The core physics-first AI platform has already been validated in a high-stakes embedded domain: real-world plasma disruption prediction using industrial microcontrollers, with 300 ms warning times and no cloud dependency. For battery management, the technology is at an adaptation stage. Seer Labs is currently proposing focused proof-of-concept work to adapt the framework to representative handheld-device data, characterize the available signals, build and optimize the on-device models for a target MCU/DSP architecture, and quantify accuracy, memory footprint, compute load, and incremental power consumption against a baseline. Following a successful PoC, the next stage is embedded integration, representative-use testing, and co-development or licensing for deployment.
About Seer Labs
Seer Labs specializes in developing Edge AI solutions specifically designed for plasma confinement in fusion reactors. The company transforms complex plasma dynamics into efficient inference engines, compressing ONNX models to run on industrial microcontrollers. By removing reliance on cloud computing and traditional operating systems, their architecture achieves near-zero latency, enabling disruption prediction with 300 ms warning times. This technology has been validated using real-world MAST M9 datasets and is designed to operate under the strict hardware constraints typical of tokamak environments.
The company focuses on providing bespoke, embedded hardware-software systems to support both national research facilities and private companies advancing magnetic confinement. Their primary objective is to move beyond passive disruption prediction toward active, closed-loop plasma control. Seer Labs offers a Hardware-in-the-Loop (HIL) development kit for potential users to verify their baseline architecture and latency performance on their own testbenches. By delivering plug-and-play hardware tailored to specific diagnostic sensors and data acquisition systems, they aim to provide the foundational infrastructure required to sustain stable plasma in commercial fusion reactors.
delivered to your inbox.