ai-accelerators

name	ai-accelerators
description	AI hardware accelerators including GPUs, TPUs, custom silicon, and hardware-aware optimization strategies for ML workloads.

AI Accelerators

Hardware acceleration for ML workloads.

Hardware Landscape

┌─────────────────────────────────────────────────────────────┐
│                    AI ACCELERATOR TYPES                      │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  GPU (NVIDIA)         TPU (Google)        NPU/Custom        │
│  ─────────────        ────────────        ─────────         │
│  CUDA cores           Systolic array      Apple Neural      │
│  Tensor cores         BF16 native         Qualcomm Hexagon  │
│  General purpose      TPU pods            Intel Habana      │
│  PyTorch/TF native    JAX optimized       AWS Inferentia    │
│                                                              │
│  FPGA                 ASIC                Edge Accelerators │
│  ─────────────        ────────────        ─────────         │
│  Reconfigurable       Fixed function      Coral Edge TPU    │
│  Low latency          Maximum perf        Jetson (NVIDIA)   │
│  Power efficient      High volume         Intel NCS2        │
│                                                              │
└─────────────────────────────────────────────────────────────┘

GPU Optimization

CUDA Memory Management

import torch

# Memory allocation
torch.cuda.empty_cache()
torch.cuda.memory_allocated()
torch.cuda.max_memory_allocated()

# Pin memory for faster transfers
train_loader = DataLoader(
    dataset,
    batch_size=32,
    pin_memory=True,
    num_workers=4
)

# Async data transfer
def async_prefetch(loader, device):
    stream = torch.cuda.Stream()
    for batch in loader:
        with torch.cuda.stream(stream):
            batch = batch.to(device, non_blocking=True)
        torch.cuda.current_stream().wait_stream(stream)
        yield batch

Tensor Core Utilization

# Ensure tensor core alignment (multiples of 8)
class TensorCoreOptimized(nn.Module):
    def __init__(self, in_features, out_features):
        super().__init__()
        # Round to multiple of 8 for tensor cores
        self.in_features = ((in_features + 7) // 8) * 8
        self.out_features = ((out_features + 7) // 8) * 8
        self.linear = nn.Linear(self.in_features, self.out_features)
        self.pad_in = self.in_features - in_features

    def forward(self, x):
        if self.pad_in > 0:
            x = F.pad(x, (0, self.pad_in))
        return self.linear(x)

# Enable TF32 on Ampere+ GPUs
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True

# Force FP16 computation
with torch.cuda.amp.autocast(dtype=torch.float16):
    output = model(input)

Multi-GPU Strategies

# DataParallel (simple, not recommended for training)
model = nn.DataParallel(model)

# DistributedDataParallel (recommended)
model = DistributedDataParallel(model, device_ids=[local_rank])

# Model Parallelism (for large models)
class ModelParallel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.TransformerEncoder(...).to('cuda:0')
        self.decoder = nn.TransformerDecoder(...).to('cuda:1')

    def forward(self, x):
        x = self.encoder(x.to('cuda:0'))
        x = self.decoder(x.to('cuda:1'))
        return x

# Pipeline Parallelism
from torch.distributed.pipeline.sync import Pipe

model = nn.Sequential(
    nn.Linear(100, 200).to('cuda:0'),
    nn.ReLU().to('cuda:0'),
    nn.Linear(200, 100).to('cuda:1')
)
model = Pipe(model, chunks=8)

TPU Optimization

# JAX/TPU optimized training
import jax
import jax.numpy as jnp
from flax import linen as nn

class TPUModel(nn.Module):
    features: int

    @nn.compact
    def __call__(self, x):
        x = nn.Dense(self.features)(x)
        x = nn.relu(x)
        return nn.Dense(10)(x)

# pmap for data parallelism across TPU cores
@jax.pmap
def train_step(state, batch):
    def loss_fn(params):
        logits = state.apply_fn({'params': params}, batch['image'])
        loss = jnp.mean(optax.softmax_cross_entropy(logits, batch['label']))
        return loss

    grad_fn = jax.value_and_grad(loss_fn)
    loss, grads = grad_fn(state.params)
    grads = jax.lax.pmean(grads, axis_name='batch')
    state = state.apply_gradients(grads=grads)
    return state, loss

# PyTorch/XLA for TPU
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl

device = xm.xla_device()
model = model.to(device)

for batch in pl.ParallelLoader(train_loader, [device]):
    output = model(batch)
    loss.backward()
    xm.optimizer_step(optimizer)

Edge Accelerators

NVIDIA Jetson

# TensorRT optimization for Jetson
import tensorrt as trt
import pycuda.driver as cuda

def build_engine(onnx_path, precision='fp16'):
    logger = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(logger)
    network = builder.create_network(
        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
    )
    parser = trt.OnnxParser(network, logger)

    with open(onnx_path, 'rb') as f:
        parser.parse(f.read())

    config = builder.create_builder_config()
    config.max_workspace_size = 1 << 30  # 1GB

    if precision == 'fp16':
        config.set_flag(trt.BuilderFlag.FP16)
    elif precision == 'int8':
        config.set_flag(trt.BuilderFlag.INT8)
        config.int8_calibrator = EntropyCalibrator(calibration_data)

    return builder.build_engine(network, config)

# DeepStream for video inference
# gst-launch-1.0 filesrc location=video.mp4 ! \
#   decodebin ! nvvideoconvert ! \
#   nvinfer config-file-path=config.txt ! \
#   nvdsosd ! nveglglessink

Coral Edge TPU

from pycoral.utils import edgetpu
from pycoral.adapters import common, classify

# Load Edge TPU model
interpreter = edgetpu.make_interpreter('model_edgetpu.tflite')
interpreter.allocate_tensors()

# Inference
common.set_input(interpreter, image)
interpreter.invoke()
classes = classify.get_classes(interpreter, top_k=5)

# Compile model for Edge TPU
# edgetpu_compiler model.tflite

TFLite for Mobile

import tensorflow as tf

# Convert to TFLite with quantization
converter = tf.lite.TFLiteConverter.from_saved_model('saved_model/')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]

# Full integer quantization
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8

tflite_model = converter.convert()

# Inference
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()

Hardware-Aware Optimization

Auto-Tuning

# TVM auto-tuning for specific hardware
import tvm
from tvm import relay, autotvm

# Extract tuning tasks
tasks = autotvm.task.extract_from_program(
    mod["main"], target="cuda", params=params
)

# Tune each task
for task in tasks:
    tuner = autotvm.tuner.XGBTuner(task)
    tuner.tune(
        n_trial=1000,
        measure_option=autotvm.measure_option(
            builder=autotvm.LocalBuilder(),
            runner=autotvm.LocalRunner(number=10)
        ),
        callbacks=[autotvm.callback.log_to_file('tune.log')]
    )

# Compile with best configs
with autotvm.apply_history_best('tune.log'):
    with tvm.transform.PassContext(opt_level=3):
        lib = relay.build(mod, target="cuda", params=params)

Hardware Selection Matrix

def select_hardware(model_size, latency_req, batch_size, budget):
    """Select optimal hardware for ML workload."""
    recommendations = []

    if model_size > 10e9:  # >10B params
        recommendations.append({
            'hardware': 'Multi-GPU (A100/H100)',
            'reason': 'Large model requires high memory bandwidth',
            'cost': 'High'
        })

    if latency_req < 10:  # <10ms
        recommendations.append({
            'hardware': 'TensorRT + GPU',
            'reason': 'Low latency requires optimized inference',
            'cost': 'Medium'
        })

    if batch_size == 1 and latency_req < 5:
        recommendations.append({
            'hardware': 'Edge TPU / Jetson',
            'reason': 'Single-sample low-latency inference',
            'cost': 'Low'
        })

    return recommendations

Benchmarking

import torch.utils.benchmark as benchmark

def benchmark_model(model, input_shape, device='cuda'):
    x = torch.randn(*input_shape).to(device)
    model = model.to(device)
    model.eval()

    # Warmup
    for _ in range(10):
        model(x)

    # Benchmark
    timer = benchmark.Timer(
        stmt='model(x)',
        globals={'model': model, 'x': x}
    )

    result = timer.blocked_autorange(min_run_time=1)

    return {
        'mean_ms': result.mean * 1000,
        'median_ms': result.median * 1000,
        'iqr_ms': result.iqr * 1000,
        'throughput': 1000 / (result.mean * 1000)
    }

Commands

• /omgoptim:profile - Profile on hardware
• /omgdeploy:edge - Edge deployment
• /omgdeploy:cloud - Cloud GPU deployment

Best Practices

1. Profile on target hardware early
2. Use hardware-specific optimizations
3. Batch for throughput, stream for latency
4. Consider power consumption for edge
5. Test with production data volumes