AI Edge Chips: Nvidia Jetson Xavier NX, AGX Xavier, Google Coral Edge TPU & Startups

Source (Google Edge TPU)

Edge Device Applications

The early adopter for AI edge chips in the consumer market will likely be computer vision in the area of object detection and classification, pose estimation, gaze detection, and image segmentation. NLP applications without the cloud are catching up like the always-on voice processors for simpler command processing.

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Nvidia Jetson AGX Xavier

Jetson AGX Xavier embedded compute module with Thermal Transfer Plate (TTP), 100x87mm
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  • 8 core Carmel ARM 64-bit CPU,
  • 512 core Volta GPU with 64 Tensor Cores,
  • dual Deep Learning Accelerator (DLA),
  • two Vision Accelerator (VA) engine,
  • HD video codecs,
  • PCIe Gen 4, and
  • 16 camera lanes of MIPI CSI-2 (128Gbps) — up to six cameras.
Carmel CPU Complex
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  • Convolution Core for convolution layers.
  • Single Data Processor — activation function lookup engine.
  • Planar Data Processor — for pooling.
  • Channel Data Processor — multi-channel averaging engine for advanced normalization functions.
  • Dedicated Memory and Data Reshape Engines — memory-to-memory transformation for tensor reshape and copy.
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Jetson AGX Xavier Developer Kit

The Jetson AGX Xavier Developer Kit will include a non-production Jetson module and a reference carrier board to host the Jetson module and the I/O. Its purpose is to have a self-contained computer system that developers can start developing applications.

Source (Right: Jetson AGX Xavier module)
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Jetson Xavier NX

Jetson Xavier NX module
Source: Nvidia
Source: Nvidia

Jetson Nano

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Modified from source

Google Coral Edge TPU

AlphaGo Zero is trained by self-play reinforcement learning to play GO to beat the GO masters.

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Block diagram of iMX 8M SoC components, provided by NXP
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
def representative_dataset_gen():
for _ in range(num_calibration_steps):
# Get sample input data as a numpy array in a
# method of your choosing.
yield [input]
converter.representative_dataset = representative_dataset_gen
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8]
tflite_quant_model = converter.convert()
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AI Edge Chip Startups

The competitions for AI Edge chips are pretty fierce in particular for startups.

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Credits & References

NVIDIA Jetson: Enabling AI-Powered Autonomous Machines at Scale

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