Intel AMX: Understanding CPU-Based Matrix Acceleration

When we think about AI acceleration, dedicated GPUs usually steal the spotlight. But Intel's Advanced Matrix Extensions (AMX) are changing that. They bring specialized matrix multiplication hardware directly into the CPU, which means you can run AI workloads faster without needing an expensive graphics card.

I've structured this guide to work for everyone. Each topic starts with a simple explanation using everyday analogies, then dives into the technical details for those who want them. Skip around as needed.

01. What is AMX?

Let me start with an analogy. Imagine you need to add up a grocery list. Traditional CPUs work like adding items one by one: apples, then oranges, then bread, then milk. It works, but it's slow.

AMX is like having a calculator that can add your entire shopping cart at once. All the items, in one go.

AI models are basically giant math problems with millions of multiplications and additions. AMX is special hardware inside newer Intel CPUs that handles many of these calculations simultaneously. The result? AI programs run much faster without needing an expensive graphics card.

AMX introduces tile-based matrix operations. Instead of processing vectors (one-dimensional arrays), AMX operates on tiles, which are two-dimensional matrix blocks that fit entirely within dedicated tile registers.

The key innovation is the TMUL (Tile Matrix Multiply) unit. This is specialized silicon dedicated exclusively to matrix operations. A single TDPBF16PSinstruction performs a complete tile multiplication, replacing what would otherwise require hundreds of traditional instructions.

02. BF16: The Perfect Precision

Here's a question: when you calculate tips at a restaurant, do you need to know the answer to 15 decimal places? Of course not. Rounding to the nearest cent is perfectly fine.

BF16 (Brain Float 16) works the same way for AI. Traditional computers use 32 bits to store numbers, giving extreme precision. But AI doesn't need that much detail. BF16 uses only 16 bits, which is half the space, while keeping enough accuracy for AI to work perfectly. Less data to move around means faster processing.

AMX processes BF16 natively in hardware. Without AMX, BF16 operations must be emulated. That means converting BF16 to FP32, doing the math, then converting back to BF16. This conversion overhead eats up all the memory benefits of using BF16 in the first place.

With AMX's native support, data stays in BF16 format throughout the entire computation pipeline. The TMUL unit handles BF16 multiplication and accumulation directly. No conversion latency, maximum memory bandwidth utilization.

03. Processing Multiple Tasks

Think of a car factory. If one worker builds an entire car alone, it takes forever. But with an assembly line, many workers handle different cars simultaneously. The factory produces many more cars per hour.

Here's the problem: Python (the programming language used for most AI) has a limitation. Only one thing can truly run at a time. It's like having an assembly line where only one worker is allowed to move at any moment.

AsyncInferQueue is the workaround. It's like moving the actual work to a separate factory floor where multiple workers can operate freely, outside of Python's restrictions.

Python's Global Interpreter Lock (GIL) prevents true multi-threaded execution. For AI inference, this creates a bottleneck. Even with fast AMX hardware, Python can only process one request at a time.

AsyncInferQueue from OpenVINO solves this by managing requests at the C++ level. Inference executes in parallel threads completely outside Python's control, fully saturating AMX capabilities.

# Create queue with multiple parallel inference slots
async_queue = AsyncInferQueue(compiled_model, jobs=16)

# Submit requests (non-blocking, GIL released)
for frame in preprocessed_frames:
    async_queue.start_async({0: frame})

# Wait for all completions
async_queue.wait_all()

04. The Software Layer: oneDNN

You don't need to know how your car engine works to drive. Similarly, oneDNN (Intel's software library) automatically uses the best CPU features available.

When you run an AI model, oneDNN checks what your CPU supports and picks the fastest method. Think of it like a smart GPS that automatically picks the fastest route based on current traffic conditions. You just tell it where to go, and it handles the rest.

oneDNN abstracts hardware complexity through its primitive selection mechanism. When compiling operations, it queries available ISAs (Instruction Set Architectures) and selects optimal implementations.

The ONEDNN_MAX_CPU_ISA environment variable provides explicit control:

# Enable all instructions including AMX
export ONEDNN_MAX_CPU_ISA=DEFAULT

# Disable AMX, use only AVX-512
export ONEDNN_MAX_CPU_ISA=AVX512_CORE_VNNI

# Fallback to basic AVX2
export ONEDNN_MAX_CPU_ISA=AVX2

Wrapping Up

If you take away just a few things from this post, let it be these:

1. AMX processes matrix blocks, not individual numbers. This architectural difference enables massive parallelism for AI workloads.
2. BF16 is native, not emulated. No conversion overhead means full memory bandwidth utilization.
3. AsyncInferQueue unlocks true parallelism. Bypassing Python's GIL is essential to saturate AMX capabilities.
4. oneDNN handles complexity automatically. Frameworks use optimal instructions without manual configuration.

AMX transforms modern Intel CPUs into capable AI inference engines. It doesn't replace GPUs for training, but it does fundamentally change the economics of CPU-based inference.

If you're running AI workloads on Intel hardware, understanding AMX can help you squeeze out significantly more performance without changing your infrastructure.