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Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design
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In the last decade, neuromorphic computing was rebirthed with the emergence of novel nano-devices and hardware-software co-design approaches. With the fast advancement in algorithms for today’s artificial intelligence (AI) applications, deep neural networks (DNNs) have become the mainstream technology. It has been a new research trend to enable neuromorphic designs for DNNs computing with high computing efficiency in speed and energy. In this chapter, we will summarize the recent advances in neuromorphic computing hardware and system designs with non-volatile resistive access memory (ReRAM) devices. More specifically, we will discuss the ReRAM-based neuromorphic computing hardware and system implementations, hardware-software co-design approaches for quantized and sparse DNNs, and architecture designs.
Title: Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design
Description:
In the last decade, neuromorphic computing was rebirthed with the emergence of novel nano-devices and hardware-software co-design approaches.
With the fast advancement in algorithms for today’s artificial intelligence (AI) applications, deep neural networks (DNNs) have become the mainstream technology.
It has been a new research trend to enable neuromorphic designs for DNNs computing with high computing efficiency in speed and energy.
In this chapter, we will summarize the recent advances in neuromorphic computing hardware and system designs with non-volatile resistive access memory (ReRAM) devices.
More specifically, we will discuss the ReRAM-based neuromorphic computing hardware and system implementations, hardware-software co-design approaches for quantized and sparse DNNs, and architecture designs.
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