visualkeras : Visualkeras is a Python package to help visualize Keras (either standalone or included in tensorflow) neural network architectures. It allows easy styling to fit most needs. As of now it supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks) and a grap style architecture.

Block diagram of neural network architecture. Neural networks have been commonly used in different domains. Hardware implementation of the neural networks is challenging...

In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to...

The purpose of this paper is to present the state of the art in neural network hardware architectures and provide a broad view of principles and practice of hardware implementation of neural networks.

Figure 3 shows a complete block diagram of the designed CNN hardware accelerator, consisting of a convolution module, a maximum pool module, and a fully connected module.

In this paper, we study various neural network architectures and propose one that is hardware-friendly for a neuromorphic hardware with crossbar array of synapses.

Convolutional Neural Network on custom hardware. A future goal for this project is to investigate the feasibility of designing custom hardware to implement the fully connected layer (neural network), which will enable the design of a complete hardware architecture for a CNN. L. Esphanhan and C. Javier Applied Research Experience in Electrical ...

Using diagrams.net (aka draw.io) to generate diagrams to better visualize neural network model architecture. Link to TowardsDataScience article: https://towardsdatascience.com/how-to-easily-draw-neural-network-architecture-diagrams-a6b6138ed875

Each block outputs the results of the feedforward operation and inputs the error from the future block in the chain for use in back-propagation mode for the network. The main building block of the design is shown below and consists of. The operation of each stage of the network is …

Hardware-based machine learning is becoming increasingly popular due to its high speed of computation. One of the desired characteristics of such hardware is reduced hardware and design costs. This paper proposes a design approach for a neural network to reduce the cost of hardware in terms of adders and multipliers.