Not my DD but FF's.
We verified RIVC's effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72)
RICV:
5.1.1. Entire Experiment Settings
This section describes the construction of the proposed framework shown in
Fig. 2. We utilized a desktop PC equipped with a GPU (Nvidia RTX3090) for
updating the policies and an Akida Neural Processor SoC as a neurochip [9, 12].
The robot was controlled by the policies implemented in the neurochip. SNNs
were implemented to the neurochip by a conversion executed by the MetaTF
of Akida that converts the software [9, 12].
Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-driven Edge Robots
A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacityWe verified RIVC's effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72)
RICV:
5.1.1. Entire Experiment Settings
This section describes the construction of the proposed framework shown in
Fig. 2. We utilized a desktop PC equipped with a GPU (Nvidia RTX3090) for
updating the policies and an Akida Neural Processor SoC as a neurochip [9, 12].
The robot was controlled by the policies implemented in the neurochip. SNNs
were implemented to the neurochip by a conversion executed by the MetaTF
of Akida that converts the software [9, 12].
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