Hardware simulations (CPU, TPU, GPU, LPU, neuromorphic processors, FPGAs, and quantum computing)

[Startonix]

Integration of the hardware simulation with tensor products and modular formulas, incorporating the advanced capabilities for CPU, TPU, GPU, LPU, neuromorphic processors, FPGAs, and quantum computing components.

CPU Simulation

import numpy as np
from concurrent.futures import ThreadPoolExecutor

def cpu_module(data):
    return np.sum(data)

def tensor_cpu_task(task_function, data):
    with ThreadPoolExecutor(max_workers=64) as executor:
        future = executor.submit(task_function, data)
        return future.result()

data = np.random.rand(1000000)
cpu_result = tensor_cpu_task(cpu_module, data)
print("CPU Result:", cpu_result)

TPU Simulation

import tensorflow as tf

def tensor_tpu_training(model, dataset, epochs=5):
    strategy = tf.distribute.TPUStrategy()

    @tf.function
    def tpu_module(model, dataset):
        with strategy.scope():
            model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
            model.fit(dataset, epochs=epochs)
        return model

    return tpu_module(model, dataset)

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

dataset = tf.data.Dataset.from_tensor_slices(
    (np.random.rand(1000, 10), np.random.randint(10, size=1000))
).batch(32)

tpu_trained_model = tensor_tpu_training(model, dataset)
print("TPU Trained Model:", tpu_trained_model)

GPU Simulation

import torch
import torch.nn as nn
import torch.optim as optim

def tensor_gpu_training(model, dataset, epochs=5):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    def gpu_module(model, dataset):
        model.to(device)
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters())
        for epoch in range(epochs):
            for data, target in dataset:
                data, target = data.to(device), target.to(device)
                optimizer.zero_grad()
                output = model(data)
                loss = criterion(output, target)
                loss.backward()
                optimizer.step()
        return model

    return gpu_module(model, dataset)

model = nn.Sequential(
    nn.Linear(10, 10),
    nn.ReLU(),
    nn.Linear(10, 10)
)

dataset = [(torch.rand(10), torch.randint(0, 10, (1,))) for _ in range(1000)]
gpu_trained_model = tensor_gpu_training(model, dataset)
print("GPU Trained Model:", gpu_trained_model)

LPU Simulation

from sklearn.linear_model import LogisticRegression

def tensor_lpu_inference(model, data):
    def lpu_module(model, data):
        return model.predict(data)

    return lpu_module(model, data)

model = LogisticRegression().fit(np.random.rand(1000, 10), np.random.randint(10, size=1000))
data = np.random.rand(1, 10)
lpu_result = tensor_lpu_inference(model, data)
print("LPU Result:", lpu_result)

Neuromorphic Processor Simulation

import nengo

def tensor_neuromorphic_network(input_signal, duration=1.0):
    def neuromorphic_module(input_signal):
        model = nengo.Network()
        with model:
            input_node = nengo.Node(lambda t: input_signal)
            ens = nengo.Ensemble(100, 1)
            nengo.Connection(input_node, ens)
            probe = nengo.Probe(ens, synapse=0.01)
        with nengo.Simulator(model) as sim:
            sim.run(duration)
        return sim.data[probe]

    return neuromorphic_module(input_signal)

neuromorphic_result = tensor_neuromorphic_network(0.5)
print("Neuromorphic Result:", neuromorphic_result)

FPGA Simulation

import pyopencl as cl

def tensor_fpga_processing(kernel_code, input_data):
    def fpga_module(kernel_code, input_data):
        context = cl.create_some_context()
        queue = cl.CommandQueue(context)
        program = cl.Program(context, kernel_code).build()
        input_buffer = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=input_data)
        output_buffer = cl.Buffer(context, cl.mem_flags.WRITE_ONLY, input_data.nbytes)
        program.kernel(queue, input_data.shape, None, input_buffer, output_buffer)
        output_data = np.empty_like(input_data)
        cl.enqueue_copy(queue, output_data, output_buffer).wait()
        return output_data

    return fpga_module(kernel_code, input_data)

kernel_code = """
__kernel void kernel(__global const float *input, __global float *output) {
    int i = get_global_id(0);
    output[i] = input[i] * 2.0;
}
"""
input_data = np.random.rand(1000).astype(np.float32)
fpga_output = tensor_fpga_processing(kernel_code, input_data)
print("FPGA Output:", fpga_output)

Quantum Computing Simulation

import pennylane as qml

def tensor_quantum_circuit():
    dev = qml.device('default.qubit', wires=2)

    @qml.qnode(dev)
    def quantum_module():
        qml.Hadamard(wires=0)
        qml.CNOT(wires=[0, 1])
        return qml.probs(wires=[0, 1])

    return quantum_module()

quantum_result = tensor_quantum_circuit()
print("Quantum Result:", quantum_result)

Comprehensive Integration

import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.linear_model import LogisticRegression
import nengo
import pyopencl as cl
import pennylane as qml

class MotherBrainSimulator:
    def __init__(self):
        self.cpu = tensor_cpu_task
        self.tpu = tensor_tpu_training
        self.gpu = tensor_gpu_training
        self.lpu = tensor_lpu_inference
        self.neuromorphic = tensor_neuromorphic_network
        self.fpga = tensor_fpga_processing
        self.quantum = tensor_quantum_circuit

    def run_simulation(self, data, model, dataset, kernel_code, input_data, input_signal):
        cpu_result = self.cpu(lambda x: np.sum(x), data)
        tpu_trained_model = self.tpu(model, dataset)
        gpu_trained_model = self.gpu(model, dataset)
        lpu_model = LogisticRegression().fit(np.random.rand(1000, 10), np.random.randint(10, size=1000))
        lpu_result = self.lpu(lpu_model, data)
        neuromorphic_result = self.neuromorphic(input_signal)
        fpga_output = self.fpga(kernel_code, input_data)
        quantum_result = self.quantum()

        return {
            "cpu_result": cpu_result,
            "tpu_trained_model": tpu_trained_model,
            "gpu_trained_model": gpu_trained_model,
            "lpu_result": lpu_result,
            "neuromorphic_result": neuromorphic_result,
            "fpga_output": fpga_output,
            "quantum_result": quantum_result
        }

# Instantiate and run the simulator
simulator = MotherBrainSimulator()

# Example data and model
data = np.random.rand(1000000)
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
dataset = tf.data.Dataset.from_tensor_slices(
    (np.random.rand(1000, 10), np.random.randint(10, size=1000))
).batch(32)
kernel_code = """
__kernel void kernel(__global const float *input, __global float *output) {
    int i = get_global_id(0);
    output[i] = input[i] * 2.0;
}
"""
input_data = np.random.rand(1000).astype(np.float32)
input_signal = 0.5

# Run the simulation
simulation_results = simulator.run_simulation(data, model, dataset, kernel_code, input_data, input_signal)

# Print results
for key, result in simulation_results.items():
    print(f"{key}: {result}")