**1. Introduction**

Benefit by the Moor's law, the von Neumann computing architecture, respectively storing and processing data instructions in the memory unit and the central processing unit (CPU), was served as the major computing model in past several decades [1]. However, physical limitations of the complementary metal-oxide-semiconductor (CMOS) technology and the storage capacity hinder the performance development of classic computers; such classic computers can no longer double its performance every 18 months, indicating the end of Moore's prediction [2].

Recently, the computing efficiency of extracting valuable information in data-intensive applications through the von Neumann computing architecture has become computationally expensive, even with super-computers [3]. The accumulated amount of energy required for the data processing through super-computers poses a query on whether the augmented performance is sustainable.

**Figure 1.** *General architecture of (a) von Neumann computing system and (b) neuromorphic computing system.*

As human beings, our brains are capable to analyze and memorize sophisticated information with only 20*W* of energy consumption [4]. In the 1980s, neuromorphic computing, proposed by Dr. Carver Mead, has matured to provide intelligent systems that able to mimic biological processes of mammalian brains through highly parallelized computing architectures; such systems typically model the function of neural network through very-large-scaled-integrated (VLSI) circuits [5]. Major differences between the von Neumann computing architecture and the neuromorphic computing system are illustrated in **Figure 1**. Recently, artificial neural networks (ANNs) have demonstrated their superior performance in many data-extensive applications, including image classification [6–8], handwritten digit recognition [9–11], speech recognition [12, 13] and others. For instance, *TrueNorth*, the neuromorphic chip fabricated by IBM in 2014, is capable to classify multiple objects within a 240 × 240-pixel video input with merely 65*mW* of energy consumption. Compared to the von Neumann computing system, such a neuromorphic computing system has five orders of magnitude more energy efficient [14]. *Loihi*, the latest prototype of brain-inspired chip fabricated by Intel in 2017, involves a mere 1/1000 power consumption of the one used by a classic computer [15].

Most recent hardware implementations on neuromorphic computing systems focus on the digital computation because of its advantages in noise immunity [16]. However, real-time data information is often recorded in the analog format; thereby, power-hungry operations, such as analog-to-digital (A/D) and digital-to-analog (D/A) conversions, are needed to facilitate the digital computation. It can be observed that the digital computation results in high power consumption with a large design area.

In this chapter, an overview of ANNs will be discussed in Section 2. Section 3 introduces the spiking information processing technique through the temporal code with the leaky integrate-and-fire neuron. Our fabricated spiking neural network chip along with its measurement results on the chaotic behavior will be demonstrated in Section 4, followed by the investigation on 3D-IC implementation technique with memristive synapses in Section 5. Applications on the chaotic time series predication and the image recognition are illustrated in Section 6.
