This project is focused on optimising the performance of neural networks by implementing them on an FPGA. By using an FPGA, we can achieve a high degree of parallelism, which can significantly improve the speed of neural network computations.
The neuron module is a hardware implementation of a neural network neuron. It takes in NUM_INPUTS inputs, each of DATA_WIDTH bits, and applies a set of weights to them. It then sums the weighted inputs and applies an activation function to produce an output. The activation function can be specified as a parameter and can be either ReLU or sigmoid.
The module has four main stages: multiplying, accumulating, activating, and waiting. In the multiplying stage, the inputs are multiplied by their corresponding weights. In the accumulating stage, the weighted inputs are accumulated. In the activating stage, the activation function is applied to the accumulated sum. In the waiting stage, the module waits for input_ready to become high, at which point it starts the next cycle.
The module uses a state machine to control the flow of data through the stages. The state machine has five states: waiting, multiplying, accumulating, activating, and done. The module starts in the waiting state and transitions to the multiplying state when input_ready becomes high. It then transitions to the accumulating state and, after accumulating all of the inputs, transitions to the activating state. Finally, it transitions to the done state, sets output_ready high, and then transitions back to the waiting state.
The weights for the inputs are initialized in an initial block in the module. The bias is also initialized in an initial block. The module supports signed inputs and weights, and the output is also signed. The module is designed to work with a clock with a period of 1ns and uses a timescale of 1ns/1ns.
The dense_layer module is a parameterized implementation of a neural network dense layer, which is designed to process a fixed number of input signals, each with a fixed data width. The module takes clock, reset, and input_ready signals as inputs and produces outputs and output_ready signals as outputs.
The module contains a generate block that instantiates a number of neuron modules equal to the specified number of neurons. Each neuron module processes the inputs using a specified activation function, and produces a single output signal. The outputs signals of the module are connected to the outputs of each of the instantiated neuron modules.
The dense_layer module has a combinatorial logic block that checks the neuron_ready signals produced by each neuron module. If any neuron module is not ready, the output_ready signal will be set to 0. Otherwise, the output_ready signal will be set to 1.
The dense_layer module is highly parameterized, allowing the user to configure the data width of the input and output signals, the number of input signals, the number of neurons in the layer, and the activation function used by the neurons.
The neural_network module is a parameterized implementation of a feedforward neural network, with a fixed number of layers and a fixed number of neurons in each layer. The module takes in clock, reset, and inputs_ready signals as inputs, and produces outputs and outputs_ready signals as outputs.
The module consists of several dense_layer submodules, each with a specified number of input and output signals and a specified activation function. The input signals to the first dense_layer module are connected to the inputs signals of the neural_network module, while the output signals of each dense_layer module are connected to the input signals of the subsequent dense_layer module. The output signals of the final dense_layer module are connected to the outputs signals of the neural_network module.
The neural_network module is highly parameterized, allowing the user to configure the data width (DATA_WIDTH) of the input and output signals, the number of layers in the network, the number of neurons (NUM_NEURONS) in each layer, and the activation function used by each layer. The sizes of each layer are specified in the SIZES parameter, while the activation functions for each hidden layer are specified in the ACTIVATIONS parameter.