Intelligence at the Edge ML
on Micro Controllers and
Embedded Devices
We optimize, quantize, and deploy machine learning models on constrained
hardware, enabling real-time, privacy-preserving inference with ultra-low
latency, minimal bandwidth usage, and reduced power consumption. Our approach
ensures high accuracy and reliability while making AI practical for edge devices and
embedded systems.
Key Value Propositions
Akhila Labs builds compliance-first, medical-grade wearables with end-to-end engineering to enable fast, scalable, and regulatory-ready digital health solutions.
Industry Problem Statement
Autonomous drones and robots must operate safely and reliably in complex, dynamicenvironments. Your team is likely facing
Cloud-centric AI pipelines are hitting significant scalability, latency, and privacy walls
Bandwidth Costs
Streaming high-frequency vibration data (1–10 kHz), video feeds, or audio to the cloud is prohibitively expensive and clogs network capacity.
Latency
In safety-critical applications (industrial safety, autonomous vehicles, medical devices), a 200 ms round-trip to the cloud is too slow to prevent accidents.
Privacy & Regulation
Smart home users and enterprises are increasingly reluctant to upload audio, video, or biometric streams to third-party servers due to GDPR, CCPA, HIPAA, and corporate policy.
Connectivity gaps
Remote or intermittent connectivity (rural areas, underground facilities, moving vehicles) makes cloud-only architectures brittle and unreliable.
The TinyML Gap
Modern neural networks require specialized optimization techniques (pruning, quantization, architecture search) that most data science teams lack. Deploying sophisticated models on 256 KB RAM devices is challenging without deep expertise.
Model Management at Scale
Managing model versions, A/B testing, and updates across fleets of embedded devices requires robust MLOps infrastructure rarely found in embedded teams.
Our Solution Approach
Model Architecture & Selection
We do not just shrink cloud models, we select and design architectures specifically for edge deployments.
Efficient Backbones
- For vision tasks, we utilize MobileNet, EfficientNet,
SqueezeNet, and ShuffleNet architectures optimized for mobile and embedded
devices.
Time-Series Models
- For sensor data, we deploy Temporal Convolutional Networks (TCNs), LSTMs, and 1D CNNs which offer better performance-per-watt than heavyTransformers for sequence modeling.
Micro-Transformers & State-Space Models
- For applications requiring advancedsequence processing on constrained devices, we pioneer optimized transformer blocksand Mamba models—state-space architectures with sub-linearcomplexity
Quantization & Pruning
We apply advanced optimization techniques to reduce model size and complexity
We optimize AI models through advanced techniques like quantization, pruning, and hardware-aware compiler optimizations to reduce model size, increase processing speed, and maintain near full-precision accuracy. Our approach ensures faster, more efficient AI systems with minimal accuracy loss, delivering high performance without compromising reliability.
Hardware Acceleration (NPU/DSP)
We leverage hardware-specific accelerators to maximize efficiency
We leverage dedicated AI accelerators, DSPs, and custom FPGA pipelines to offload compute-intensive workloads, delivering massive performance gains, ultra-low latency, and power-efficient edge AI processing.
MLOps & Fleet Management
We Build Over-The-Air (OTA) Pipelines for Robust Model Lifecycle Management
We ensure reliable AI deployments through automated model versioning and testing, staged rollouts with safe fallbacks, continuous telemetry and monitoring, and federated learning to improve models while preserving data privacy.
Connectivity & Data Flow
BLE/Wi-Fi/NB-IoT for events and alerts when needed
Our architecture combines device-level and edge-level connectivity using BLE, Wi-Fi, NB-IoT, Ethernet, and 5G to transmit alerts, events, and aggregated insights only when required. With adaptive, event-based reporting and local thresholding.
Use Cases & Applications
Akhila Labs supports a wide spectrum of healthcare and wellness applications:
Predictive maintenance
On-device vibration analysis and anomaly detection onfactory equipment, without continuous cloudconnectivity.
Smart agriculture
IoT nodes for crop/soil/environmental anomaly detection with local decision-making and minimal cloud dependency.
Smart sensor networks
Building occupancy, HVAC control, and lighting optimizationusing on-device ML for instant responses and reduced energy waste.
Autonomous vehicles
On-board object detection, lane following, and hazard identification with millisecond-level latency.
Always-on wake-word detection
Microphone-equipped devices listening for activationphrases (e.g., “Alexa,” “Hey Siri”) using ultra-low-power local inference.
Wearable health
Smartwatches and medical bands running TinyML for arrhythmiadetection, fall detection, and activity classification on-device.
Visual anomaly detection
Cameras flagging defects, intrusions, or zone breaches inreal-time without uploading video streams.
Industrial robotics
Edge inference for vision-guided robotic arms and autonomous material handling systems.
Condition monitoring
Energy meters, transformers, and utility equipment using edge AI to detect faults and unusual patterns locally
Quality inspection
Embedded vision in manufacturing detecting defects (cracks,assembly errors) on production lines with instant feedback.
Technologies & Tool
Microcontroller & Edge Hardware
STM32, nRF52/nRF53, ESP32, ARM Cortex-M0/M4/M7/M33/M55, Ambiq Apollo, RP2040
ML Frameworks & Tools
TensorFlow Lite for Microcontrollers, ONNX Runtime for Edge, Edge Impulse, PyTorch Mobile,CMSIS-NN, Apache TVM
Accelerators & NPUs
Arm Ethos-U55/U65, Hailo-8/10, Qualcomm Hexagon DSP, Coral TPU, Xilinx Kria/Zynq
RTOS & OS
FreeRTOS, Zephyr, Bare-metal, Embedded Linux (Yocto), TinyOS
Development & Training
Python (TensorFlow, PyTorch, scikit-learn), MATLAB/Simulink, Edge Impulse Studio, VS Code
Deployment & Monitoring
TensorRT (optimization), Vela Compiler, OTA update frameworks, fleet managemen
Frequently Asked Questions
TinyML and traditional machine learning differ mainly in where and how models run. Traditional ML relies on powerful servers or cloud infrastructure to process large datasets and perform inference, requiring continuous connectivity and higher energy consumption. In contrast, TinyML runs lightweight ML models directly on ultra-low-power edge devices such as microcontrollers
How much accuracy is lost when quantizing a model to INT8?
With proper quantization-aware training (QAT), accuracy loss is typically <1–2%.Post-training quantization (PTQ) may lose 2–5% depending on the model and dataset. Wevalidate every quantized model on your specific use case.
Can I deploy a pre-trained model (e.g., from TensorFlow Hub) on my MCU?
Sometimes, but not always. Many off-the-shelf models are too large or complex. We typicallyfine-tune, compress, or redesign models to fit your hardware constraints while maintainingaccuracy for your specific task.
What is model quantization, and why does it matter?
Quantization converts a model's weights and activations from 32-bit floats to 8-bit or 4-bitintegers, reducing model size by 4–8x and speeding up inference by 3–10x. This makesdeployment on MCUs and edge devices feasible.
Can edge AI models be updated in the field?
Yes. We implement secure OTA pipelines where new models are signed, verified, anddeployed to devices over the network. Rollback mechanisms ensure you can revert if a newmodel degrades performance.
How do you handle model monitoring and drift detection?
We implement telemetry that tracks inference latency, memory usage, and (where possible)accuracy metrics. If performance degrades, we alert teams and can trigger model retraining orrollback.
What if my use case requires a Transformer or large language model?
For simple use cases, we explore efficient transformer variants or state-space models(Mamba). For complex LLM tasks, we use knowledge distillation to create smaller studentmodels that mimic teacher LLM behavior.
How much power does edge AI inference consume?
Highly variable. A single inference on a Cortex-M4 might consume 10–100 µJ. An Ethos-U55can deliver higher throughput at <1 mJ per inference. We profile your specific workload toprovide accurate power budgets.
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