Business need and fit
A terminal may need to detect a defined object, track count changes or supply events to a business application. Putting a model on the device also requires camera handling, system dependencies, inference runtimes, memory management, thermal assessment and recovery from faults.
ASWORK provides embedded and edge AI engineering to connect models, device applications and business interfaces on target hardware. This scope suits companies with an existing device platform or a defined hardware direction that need vision algorithms integrated into an operational terminal.
How local processing connects to the business
For specified object detection in a fixed region, the proposed device workflow links image capture, inference and event handling.
- Acquire camera images and perform the required format conversion, resizing and preprocessing.
- Run the adapted model locally and return classes, locations and data needed for event decisions.
- Record event time, state and device identity under business rules, exposing results to the application interface.
- Cache data during connectivity failures where configured, then retry transmission and record outcomes after recovery.
Caching needs defined capacity, retention and duplicate-handling rules. Retaining source images depends on purpose, storage resources and data management requirements.
Development and integration scope
- Device adaptation covering system releases, camera pipelines, drivers and inference runtimes.
- Model conversion, input and output checks, inference integration and relevant performance optimization.
- Application logic connecting detections, event rules, local interfaces and uploads.
- Device logs, health status, startup recovery, configuration management and update rollback.
Embedded Linux or Android applications and interface work can be included as scoped. CPU, GPU or NPU execution depends on hardware support, model operations and runtime compatibility.
Project scope and handover
An initial project can use one target board, one camera and a defined detection task to validate the pipeline. Deliverables may include the device application, adapted model, interface examples, agreed source code, dependency records, deployment instructions and target-device test results.
Evaluation covers end-to-end latency, sustained throughput, memory, temperature and power, together with network loss, restarts, camera faults and extended operation. Model outputs before and after conversion also need comparison on representative inputs.
What to prepare
Provide board and operating system details, camera specifications, the model or task definition, sample inputs, business interfaces and installation conditions. State the target resolution, processing frequency, power and cooling conditions, and offline requirements.
Hardware purchase, custom drivers, model retraining and installation need separate scope decisions. Actual speed and stability are established under named hardware, software versions and input conditions.
Frequently asked questions
Can the terminal keep working without a network?
Functions deployed locally without online dependencies can be designed to continue. Cloud queries, remote model calls and uploads need their own offline behavior, and cache capacity must be defined.
Can an existing model run directly on the target chip?
Its format, operations, inputs, outputs and runtime compatibility need assessment. Conversion or architecture changes may be necessary; nominal chip compute figures alone do not establish suitability.
Can one device process multiple cameras?
This can be assessed against hardware, resolution, capture rate, model cost and cooling. The supported channel count and sustained processing capability require whole-device testing.