Titlebook: Neuromorphic Computing and Beyond; Parallel, Approximat Khaled Salah Mohamed Book 2020 Springer Nature Switzerland AG 2020 cognitive comput

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Reconfigurable and Heterogeneous Computing,vices are usually controlled by a microprocessor that executes the instructions stored on a read-only memory (ROM) chip. The software for the embedded system is called firmware. Embedded systems are also known as real-time systems since they respond to an input or event and produce the result within a guaranteed time period [1].

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Deep Learning and Cognitive Computing: Pillars and Ladders,s in difficult machine learning problems such as image or object recognition, speech recognition and machine language translation. Now machine learning is everywhere. Facebook as an example, machine learning in search, face tagging, advertisement, and news feed.

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Approximate Computing: Towards Ultra-Low-Power Systems Design,accurate result to save resources, memory, run-time, and energy. The motivation behind this is that some applications are computing their results more accurately than needed, so they waste resources. So, it trades off between accuracy in computation or acceptable quality of results and resources [1]

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An Introduction: New Trends in Computing,manufacturing cost. As Gordon Moore predicted in his seminal paper, reducing the feature size also allows chip area to be decreased, improving production, and thereby reducing cost per function. The scaling laws showed that improved device and ultimately processor speed could be achieved through dim

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Deep Learning and Cognitive Computing: Pillars and Ladders, machine learning that deals with algorithms inspired by the structure and function of the brain. Machine learning is a subset of artificial intelligence as shown in Fig. 4.1. Deep neural networks (DNNs) are a family of neuromorphic computing architectures that have recently made substantial advance