ALU - PROGRAMMABLE FIR FILTER

  import numpy as np
from scipy import signal

def generate_lowpass(num_taps, cutoff, coef_fract_bits=15):
    """Generate low-pass filter coefficients."""
    coefficients = signal.firwin(num_taps, cutoff, window='hamming')
    scale = 2 ** coef_fract_bits
    quantized = np.round(coefficients * scale).astype(int)
    return quantized

# Generate 16-tap low-pass with cutoff at 0.2 * Nyquist
coefs = generate_lowpass(16, 0.2)
print("Low-pass coefficients:", list(coefs))
  

  import numpy as np

class LMSFilter:
    """Least Mean Squares adaptive filter for hardware implementation."""

    def __init__(self, num_taps, mu=0.01, coef_fract_bits=15):
        self.num_taps = num_taps
        self.mu = mu  # Step size
        self.coef_fract_bits = coef_fract_bits
        self.scale = 2 ** coef_fract_bits
        self.weights = np.zeros(num_taps)
        self.buffer = np.zeros(num_taps)

    def update(self, x, d):
        """
        Update filter with new sample.
        x: input sample
        d: desired output
        Returns: (output, error, quantized_weights)
        """
        # Shift buffer
        self.buffer = np.roll(self.buffer, 1)
        self.buffer[0] = x

        # Compute output
        y = np.dot(self.weights, self.buffer)

        # Compute error
        e = d - y

        # Update weights (LMS)
        self.weights += 2 * self.mu * e * self.buffer

        # Quantize weights for hardware
        quantized = np.round(self.weights * self.scale).astype(int)

        return y, e, quantized

    def get_coefficients(self):
        """Get current coefficients in hardware format."""
        return np.round(self.weights * self.scale).astype(int)

# Example usage
lms = LMSFilter(num_taps=16, mu=0.01)

# Simulate adaptation (in practice, run on actual signals)
for i in range(1000):
    x = np.random.randn()  # Input
    d = x * 0.5  # Desired output (example: gain of 0.5)
    y, e, coefs = lms.update(x, d)

print("Adapted coefficients:", list(lms.get_coefficients()))
  

  import numpy as np

def generate_serial_sequence(coefficients, coef_bits=16):
    """
    Generate sequence of (addr, data, wr) tuples for SERIAL mode loading.
    """
    sequence = []
    for addr, coef in enumerate(coefficients):
        # Ensure coefficient fits in specified bits (signed)
        max_val = 2 ** (coef_bits - 1) - 1
        min_val = -2 ** (coef_bits - 1)
        coef_clamped = max(min_val, min(max_val, int(coef)))

        sequence.append({
            'addr': addr,
            'data': coef_clamped,
            'wr': 1
        })

    # Add LOAD pulse at end
    sequence.append({
        'addr': 0,
        'data': 0,
        'wr': 0,
        'load': 1
    })

    return sequence

# Example: Generate loading sequence
from scipy import signal
coefs = signal.firwin(16, 0.3) * (2**15)
coefs = coefs.astype(int)

sequence = generate_serial_sequence(coefs)
for step in sequence:
    print(step)
  

  import numpy as np
from scipy import signal

class FilterBank:
    """Pre-computed filter bank for runtime switching."""

    def __init__(self, num_taps, coef_fract_bits=15):
        self.num_taps = num_taps
        self.scale = 2 ** coef_fract_bits
        self.filters = {}

    def add_lowpass(self, name, cutoff):
        """Add low-pass filter to bank."""
        coefs = signal.firwin(self.num_taps, cutoff, window='hamming')
        self.filters[name] = np.round(coefs * self.scale).astype(int)

    def add_highpass(self, name, cutoff):
        """Add high-pass filter to bank."""
        coefs = signal.firwin(self.num_taps, cutoff, window='hamming', pass_zero=False)
        self.filters[name] = np.round(coefs * self.scale).astype(int)

    def add_bandpass(self, name, low, high):
        """Add band-pass filter to bank."""
        coefs = signal.firwin(self.num_taps, [low, high], window='hamming', pass_zero=False)
        self.filters[name] = np.round(coefs * self.scale).astype(int)

    def add_custom(self, name, coefficients):
        """Add custom filter coefficients."""
        self.filters[name] = np.round(np.array(coefficients) * self.scale).astype(int)

    def get(self, name):
        """Get filter coefficients by name."""
        return self.filters.get(name)

    def list_filters(self):
        """List available filters."""
        return list(self.filters.keys())

# Create filter bank
bank = FilterBank(num_taps=32)
bank.add_lowpass('lp_narrow', 0.1)
bank.add_lowpass('lp_wide', 0.4)
bank.add_highpass('hp_dc_block', 0.05)
bank.add_bandpass('bp_mid', 0.2, 0.4)

# Get coefficients for hardware
print("Available filters:", bank.list_filters())
print("LP narrow:", list(bank.get('lp_narrow')))
  

  import numpy as np
from scipy import signal

def generate_equalizer(num_taps, bands, gains, fs=1.0, coef_fract_bits=15):
    """
    Generate equalizer coefficients.

    bands: List of (low, high) frequency pairs (normalized 0-1)
    gains: List of gains for each band (linear, not dB)
    """
    # Create frequency/gain specification
    freq = [0]
    gain = [gains[0] if bands[0][0] == 0 else 0]

    for (low, high), g in zip(bands, gains):
        freq.extend([low, high])
        gain.extend([g, g])

    freq.append(1)
    gain.append(gains[-1] if bands[-1][1] == 1 else 0)

    # Design filter
    coefs = signal.firwin2(num_taps, freq, gain)

    scale = 2 ** coef_fract_bits
    return np.round(coefs * scale).astype(int)

# Example: 3-band equalizer
# Bass boost, mid cut, treble boost
bands = [(0, 0.15), (0.15, 0.5), (0.5, 1.0)]
gains = [1.5, 0.7, 1.3]  # Linear gains

eq_coefs = generate_equalizer(32, bands, gains)
print("Equalizer coefficients:", list(eq_coefs))
  

  def estimate_update_time(num_taps, mode, clock_freq_mhz):
    """
    Estimate time to update all coefficients.

    mode: 'parallel' or 'serial'
    clock_freq_mhz: System clock frequency in MHz
    """
    clock_period_ns = 1000 / clock_freq_mhz

    if mode == 'parallel':
        # Single clock cycle for all coefficients
        cycles = 1
    else:
        # N cycles to write + 1 for LOAD
        cycles = num_taps + 1

    time_ns = cycles * clock_period_ns
    time_us = time_ns / 1000

    print(f"Mode: {mode}")
    print(f"Clock: {clock_freq_mhz} MHz ({clock_period_ns:.2f} ns period)")
    print(f"Taps: {num_taps}")
    print(f"Update cycles: {cycles}")
    print(f"Update time: {time_ns:.2f} ns ({time_us:.3f} us)")

    return cycles, time_ns

# Compare modes for 32-tap filter at 100 MHz
estimate_update_time(32, 'parallel', 100)
print()
estimate_update_time(32, 'serial', 100)