FPGA – Electronic Design

FPGA-based Audio Digital to Analog Converter (DAC)

Specifications

Input : I2S, bitdepth from 16 to 32 bits, sampling rate upto 768khz,
Asynchronous FIFO built-in,
FPGA-based delta-sigma digital to analog converter (DAC),
256-step digital volume,
Full balanced outputs,
Compatible with Raspberry Pi 3/3B+/4 connection,
Ultra-low phase noise oscillators with high precision linear low drop voltage regulators are designed specifically for High-Definition audio (HD audio).

Schematic and PCB design

Schematic and PCB were designed using Free open software KiCad

https://www.kicad.org/

Power supply for FPGA Cyclone IV

Oscillator block with 2 separated oscillators. Each one will provide clock for each sampling rate family (44.1 or 48)

3rd order Low pass filter

PCB layout with 4 layers

FPGA programming

(to be continued…)

Implementing a FIR filter on FPGA

FIR (Finite impulse response) filter

Overview

Finite impulse response (FIR) filters are characterized by the fact that they use only delayed versions of the input signal to filter the input to the output.
For a causal discrete-time FIR filter of order N, each value of the output sequence is a weighted sum of the most recent input values:

Where:

x[n] is the input signal,
y[n] is the output signal,
N is the filter order
bi is the value of the impulse response, also a coefficient of the filter

Filter design

FIR filter designing is finding the coefficients and filter order that meet certain specifications. When a particular frequency response is desired, several different design methods are common:

Window design method
Frequency sampling method
Least MSE (mean square error) method
Parks-McClellan method
DFT algorithms

There many software such as MATLAB, GNU Octave, Scilab and SciPy (Python).
In this topic, I only share how to implement FIR filter on FPGA. So the detail of filter designing, finding coefficients will shared in the later topics.

Implementing a FIR filter on FPGA for slow signal

FIR filter for slow signal will designed by using special way to save the number of multipliers.

Finding coefficients using window method

Using Python

from future import print_function
from future import division
import numpy as np

fS = 48000 # Sampling rate.
fL = 22000 # Cutoff frequency.
N = 13 # Filter length, must be odd.

h = np.sinc(2 * fL / fS * (np.arange(N) – (N – 1) / 2))

h /= np.sum(h)
print(h)

2. Implementing on FPGA

(Finite-state machine with datapath of slow FIR filter)

(Simulation result – using Python + QuestaSim)

Implementing a CIC filter on FPGA

Cascaded integrator–comb filter

Overview

In digital signal processing, a cascaded integrator–comb (CIC) is an optimized class of finite impulse response (FIR) filter combined with an interpolator or decimator.

A CIC filter consists of one or more integrator and comb filter pairs. In the case of a decimating CIC, the input signal is fed through one or more cascaded integrators, then a down-sampler, followed by one or more comb sections (equal in number to the number of integrators). An interpolating CIC is simply the reverse of this architecture, with the down-sampler replaced with a zero-stuffer (up-sampler).

The system function for the composite CIC filter referenced to the high sampling rate, fs is: