AI v/s Traditional Processors

Audience: Everyone

AI-based processors, also known as AI accelerators or neural processing units (NPUs), are specialized hardware designed to perform AI-related computations efficiently. These processors are optimized to handle the computational demands of artificial intelligence tasks, such as machine learning, deep learning, and neural network inference.

AI-based processors differ from general-purpose processors in several key ways:

1. Architecture: AI processors often feature specialized architectures that are specifically designed to accelerate AI workloads. They may include dedicated hardware units for matrix multiplication, parallel processing, and data movement optimized for neural network computations.

Example:

Google's Tensor Processing Unit (TPU) is an example of a specialized architecture for AI workloads. It features a custom-built matrix multiplication unit and is specifically designed to accelerate neural network computations.

2. Parallelism: AI workloads heavily rely on parallel processing, as neural networks involve large-scale matrix operations and processing of multiple data points simultaneously. AI processors are typically designed to efficiently execute parallel operations, leveraging techniques such as vector processing or tensor processing units (TPUs).

Example:

NVIDIA's Graphics Processing Units (GPUs) are widely used for AI computations due to their high parallel processing capabilities. GPUs excel in performing parallel operations on large matrices, making them well-suited for neural network training and inference.

3. Memory Hierarchy: AI processors often have specialized memory hierarchies that prioritize data access patterns commonly found in AI workloads. This may include on-chip high-bandwidth memory (HBM) or specialized caches to minimize data movement and improve overall performance.

Example:

Graphcore's Intelligence Processing Unit (IPU) incorporates a unique memory architecture called the Intelligent Memory Fabric. It provides high-bandwidth access to data, enabling efficient movement of tensors in and out of the processor for AI computations.

4. Instruction Set: AI processors may introduce new instructions or extensions to support AI-specific operations efficiently. These instructions can accelerate key computations involved in neural network training or inference, resulting in improved performance and energy efficiency.

Example:

ARM's Neoverse N1 platform includes the Scalable Vector Extension (SVE) instruction set. SVE offers scalable vector processing capabilities, enabling efficient execution of AI workloads on ARM-based processors.

5. Power Efficiency: AI processors are optimized for power efficiency due to the significant computational demands of AI workloads. These processors often employ techniques such as reduced precision arithmetic (e.g., INT8 or INT4) or quantization to minimize power consumption while maintaining acceptable accuracy levels.

Example:

Qualcomm's Hexagon DSP (Digital Signal Processor) is designed to provide efficient and low-power processing for AI tasks on mobile devices. It incorporates specialized instructions and hardware optimizations to deliver high-performance AI computations while maintaining power efficiency.

AI-based processors enable faster and more efficient execution of AI algorithms compared to traditional general-purpose processors. They are designed to handle the massive computational requirements and specialized data processing needs of AI workloads, allowing for improved performance, reduced latency, and increased energy efficiency in AI applications.