Neural Processing Unit (NPU) - Component for AI Acceleration

Product Specifications

Technical details and manufacturing context for Neural Processing Unit

Definition

A Neural Processing Unit (NPU) is a microprocessor specifically designed to accelerate neural network computations, particularly for artificial intelligence and machine learning applications. As part of a System-on-chip (SoC), it handles tasks like inference and training of deep learning models with high energy efficiency, offloading these computationally intensive operations from the main CPU or GPU to optimize performance and power consumption in devices such as smartphones, edge computing devices, and AI accelerators. The NPU operates by executing matrix and vector operations fundamental to neural networks through parallel processing architectures. It uses specialized circuits like systolic arrays or tensor cores to perform massive parallel computations on multi-dimensional data (tensors). The unit typically includes dedicated memory hierarchies and dataflow engines that minimize data movement, implementing algorithms such as convolution, pooling, and activation functions with hardware-level optimizations for low latency and high throughput. Typical specifications for industrial-grade NPUs include computing power of 8–32 TOPS (INT8 precision), power consumption of 5–25 W, operating temperature range of -40 to 85 °C, supply voltage of 0.8–1.2 V, process node of 7–16 nm, memory bandwidth of 25.6–204.8 GB/s (LPDDR4x or LPDDR5), package dimensions of 15×15 to 45×45 mm (FCBGA or similar), weight of 5–50 g, operating humidity of 10–90% RH (non-condensing), and ESD tolerance of ±2000 V (HBM model, per IEC 61000-4-2). These values are directory reference ranges and must be confirmed for the specific model and application. The NPU is fabricated on silicon and is intended for integration into electronic systems. For procurement, verify model-specific parameters, interfaces, and compliance with relevant standards with the legal manufacturer or supplier.

Working Principle

The NPU executes neural network workloads by performing massive parallel matrix and vector operations using specialized hardware such as systolic arrays or tensor cores. It processes multi-dimensional data (tensors) with dedicated memory hierarchies and dataflow engines to minimize data movement. Algorithms like convolution, pooling, and activation functions are implemented with hardware-level optimizations, achieving low latency and high throughput for inference and training tasks.

Common Materials

Silicon

Technical Parameters

Parameter	Typical range	Notes & selection driver
Computing Power	8–32 TOPS	INT8 precision typical
Power Consumption	5–25 W	Depends on workload and process node
Operating Temperature	-40–85 °C	Industrial grade
Supply Voltage	0.8–1.2 V	Core voltage; I/O may differ
Process Node	7–16 nm	Smaller node improves efficiency
Memory Bandwidth	25.6–204.8 GB/s	LPDDR4x or LPDDR5
Package Dimensions	15×15–45×45 mm	FCBGA or similar
Weight	5–50 g	Including package and heat spreader
Operating Humidity	10–90 % RH	Non-condensing
ESD Tolerance	±2000 V	HBM modelIEC 61000-4-2

Ranges are indicative industry figures for RFQ preparation, not a supplier commitment. Confirm every value and standard with the legal manufacturer before ordering.

Components / BOM

Processing Element Array
Parallel computation units that perform matrix multiplications and convolutions

Material: silicon
On-Chip Memory Part
High-speed cache memory for storing weights, activations, and intermediate results

Material: silicon
Control Unit
Manages instruction execution, data flow, and coordination between processing elements

Material: silicon
Interconnect Fabric
Network connecting processing elements and memory hierarchy for efficient data movement

Material: copper/silicon

Industry Taxonomies & Aliases

Commonly used trade names and technical identifiers for Neural Processing Unit.

Applied To / Applications

This component is essential for the following industrial systems and equipment:

System-on-chip

View system integration details

Manufacturers of Neural Processing Unit

1 company lists this product among what they make. Company figures are quoted from each company's own website; every card states where the relationship came from.

Geniatech

Shenzhen, Guangdong, CN

Also makes: Human-Machine Interface, Industrial IoT Gateway, AI Accelerator Module and 4 more

Listed on the company's own website · profile compiled by CNFX from public sources

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Procurement Evaluation Criteria

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Technical documentation

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Frequently Asked Questions

What is the typical computing power of an NPU?

The directory lists a computing power range of 8–32 TOPS at INT8 precision. This is a reference range; the actual value depends on the specific NPU model and configuration. Always confirm with the manufacturer.

What are the power consumption and thermal requirements?

Power consumption is typically 5–25 W, and the operating temperature range is -40 to 85 °C. These values are indicative and must be verified for the specific model, as they depend on workload and process node.

Which memory interfaces are supported?

The NPU supports LPDDR4x or LPDDR5 memory, with a memory bandwidth range of 25.6–204.8 GB/s. The exact interface and bandwidth depend on the NPU implementation and system design.

How should I verify ESD tolerance and compliance?

The listed ESD tolerance is ±2000 V (HBM model) per IEC 61000-4-2. This is a reference standard; the actual ESD performance and compliance should be confirmed with the manufacturer for the specific NPU model.

Data Basis

Editorial classification, named public sources where available, and source-reviewed manufacturer records.

Preliminary Technical Classification

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humidity:	5% to 95% non-condensing
pressure:	Atmospheric pressure (sealed package), no pressure rating required
temperature:	0°C to 85°C (operational), -40°C to 125°C (storage)
power consumption:	5W to 300W depending on configuration

Neural Processing Unit

Product Specifications

Industry Taxonomies & Aliases

Applied To / Applications

Industrial Ecosystem & Supply Chain Structure

Application Fit & Sizing Matrix

Reliability & Engineering Risk Analysis

Compliance & Manufacturing Standards