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Computer Vision Systems • Edge Deployment (MobileNet, EfficientNet-Lite)Medium⏱️ ~2 min

EfficientNet Lite: Compound Scaling for Hardware Constrained Deployment

COMPOUND SCALING

Traditional scaling increases depth (more layers), width (more channels), or resolution independently. EfficientNet scales all three together using a compound coefficient φ: depth scales by 1.2^φ, width by 1.1^φ, resolution by 1.15^φ. This balanced approach achieves better accuracy per FLOP than scaling any dimension alone. EfficientNet-B0 through B7 represent increasing φ values.

EFFICIENTNET LITE MODIFICATIONS

Standard EfficientNet uses operations not well-supported on mobile hardware. EfficientNet-Lite makes three changes: (1) Replace Swish activation with ReLU6, which is 2-3x faster on mobile accelerators. (2) Remove squeeze-and-excite blocks, which require global pooling that is slow on edge TPUs. (3) Fix input resolution to avoid shape-dependent optimization issues. These changes sacrifice 1-2% accuracy for 40-60% faster inference on edge devices.

CHOOSING THE RIGHT VARIANT

EfficientNet-Lite0: 5ms on mobile GPU, 75% ImageNet top-1. Good for classification with tight latency.
EfficientNet-Lite2: 12ms, 77% accuracy. Balanced choice.
EfficientNet-Lite4: 30ms, 80% accuracy. Maximum accuracy when latency budget allows.

💡 Key Insight: EfficientNet-Lite achieves similar accuracy to ResNet-50 (80%) at 10x fewer FLOPs, making it ideal for edge deployment.

QUANTIZATION COMPATIBILITY

EfficientNet-Lite is designed for INT8 quantization. The simplified architecture (no squeeze-excite, ReLU6) quantizes cleanly with less than 1% accuracy loss, enabling 2-4x speedup on integer-only accelerators.

💡 Key Takeaways
✓Compound scaling: depth × 1.2^φ, width × 1.1^φ, resolution × 1.15^φ achieves better accuracy per FLOP
✓Lite modifications: ReLU6 (2-3x faster), no squeeze-excite, fixed resolution; trades 1-2% accuracy for 40-60% faster inference
✓Variants: Lite0 (5ms, 75%), Lite2 (12ms, 77%), Lite4 (30ms, 80%) for different latency budgets
✓Quantization-friendly: INT8 with <1% accuracy loss, 2-4x speedup on integer accelerators
📌 Interview Tips
1Explain compound scaling: scaling depth, width, and resolution together beats scaling any one dimension
2Describe Lite modifications: Swish→ReLU6, remove squeeze-excite, fix resolution for edge hardware
3Use the variant selection: Lite0 for tight latency, Lite4 for maximum accuracy with 30ms budget
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