Compare Edge ML Performance: TensorFlow Lite on Pi 5 vs ESP32-S3 Kit

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

You'll deploy identical image classification models onto a Raspberry Pi 5 and an ESP32-S3, then measure and compare inference time, accuracy, and power consumption - mimicking the workflow of real-world embedded ML engineers evaluating hardware for on-device AI. This kit turns a messy multi-vendor sourcing task into a streamlined research-grade comparison tool.

What You'll Build

A dual-platform edge inference testbench that captures images with two cameras, runs optimized TensorFlow Lite models, logs performance metrics, and generates comparison charts. You'll produce a data-driven report identifying the best processor for low-latency, low-power image classification.

What You'll Learn

• Optimise models for Arm Cortex-A76 and Xtensa LX7 targets
• Measure inference time in Python and C++
• Profile power draw and correlate with model complexity
• Analyse accuracy trade-offs between int8 and float32 quantized models

Kit Contents