Neural Processing Units are specialized hardware accelerators designed to speed up AI and machine learning workloads. They've become essential components in modern chip design, but they come with critical limitations. That's why we created the General-Purpose Neural Processing Unit, or GPNPU.
An NPU (Neural Processing Unit) is a specialized hardware block used in semiconductor chip design to accelerate the performance of machine learning and artificial intelligence workloads. NPUs are optimized for the mathematical operations — particularly matrix multiplications (often called "MatMul" for short) — that form the foundation of neural networks.
Around 2015, chip designers recognized a fundamental problem: the emerging wave of AI and machine learning algorithms wouldn't run efficiently on existing processors. CPUs, GPUs, and DSPs weren't designed for the unique computational patterns of neural networks.
Neural networks rely heavily on matrix multiplication: performing millions of multiply-accumulate operations in parallel. Traditional processors can technically perform these operations, but at very low efficiency and speed. Purpose-built hardware is required.
NPUs emerged as dedicated accelerators that could execute AI inference operations orders of magnitude faster than other classes of processors while consuming significantly less power.
Pre-2015
AI workloads ran on general-purpose processors, limited by architecture inefficiencies.
2015-2020
Dedicated accelerators emerged to handle matrix-heavy AI computations efficiently.
2020+
Programmable processors combine NPU performance with software flexibility.
NPUs accelerate specific operations repeatedly used by neural networks.
Optimized hardware for the dense matrix operations at the heart of neural networks.
Efficient execution of convolutional layers used in image recognition and computer vision.
Hardware support for common activation functions like ReLU, sigmoid, and softmax.
Accelerated max pooling and average pooling operations for dimensionality reduction.
Flow Control / Coordination / Op Fallback
Matrix Ops
Signal Processing
NPUs function as accelerators, offloading specific operations so the host CPU can coordinate the overall workload.
While NPUs deliver impressive performance for supported operations, their fixed-function architecture creates challenges for chip designers building products with multi-year lifecycles.
NPUs support a predetermined list of operators; new AI operators require updates to the silicon.
NPUs work alongside host CPUs but can't execute complete AI workloads independently.
Complex models are split across the NPU, CPU, and DSP, complicating integration.
Each processor (NPU, CPU, and DSP) requires its own compiler, debugger, and development workflow that must be synchronized against the others.
When new AI models require operators not built into the NPU, those operators fall back to the CPU and performance craters. For latency-sensitive workloads, this can render the model effectively unusable.
95% runs on NPU
60% NPU, 40% falls back to CPU
80% falls back to CPU
General-Purpose NPUs represent the evolution of AI silicon, combining the performance of NPUs with the flexibility of programmable processors.
Discover how Quadric's Chimera™ GPNPU delivers NPU-class performance with full programmability—no companion processors required.
