How does the M4's Neural Engine performance compare to Google's Tensor Processing Units (TPUs)

Architecture and Design

The Apple M4 Neural Engine consists of 16 neural cores designed to handle AI workloads efficiently. It is integrated within the Apple M4 SoC, which is built on a 3 nm technology node, and includes other components such as a 9-core CPU and a 10-core GPU. The Neural Engine is designed to deliver high throughput with low latency for ML models optimized for Apple's hardware ecosystem. It supports a peak performance of approximately 38 trillion operations per second (TOPS) according to available reports. The M4 chip is positioned to run on macOS and iPadOS platforms and benefits from Apple's unified and tightly integrated hardware-software optimization.

Google's TPUs, on the other hand, are specialized ASICs designed explicitly for accelerating TensorFlow-based ML workloads. In consumer devices, Google has integrated TPU cores within its Tensor SoCs, such as the Tensor G4, which features an Edge TPU specialized for AI tasks. The Google Tensor G4 chip itself uses a 4 nm process node and includes an 8-core CPU and an ARM Immortalis GPU. Google's TPU devices focus heavily on AI inference acceleration, especially for deep learning and large language model tasks. Google TPUs are also a cornerstone of Google Cloud's AI infrastructure, but in the consumer context, they are optimized for Android and Google services integration.

Performance

The Apple M4 Neural Engine is rated at around 38 TOPS of AI performance, a significant upgrade from its predecessors. This performance is dedicated solely to neural computations independent of the CPU and GPU cores. The M4's Neural Engine is reported to be about 4.7 times faster on certain AI benchmarks compared to the Neural Engine in the M1, reflecting advancements in efficiency and throughput. It excels in tasks such as image recognition, on-device language processing, and augmented reality computations.

Google's TPU performance varies depending on the version and application. The TPU cores integrated into the Tensor G4 chip provide AI acceleration tuned for edge-device ML applications. While precise TOPS ratings for the G4 Edge TPU in consumer devices are less public, TPU architectures generally deliver high efficiency for tensor operations, which is the mathematical foundation of neural networks. Google's TPU designs emphasize specialized matrix multiplication units and quantized neural network support, targeting specific deep learning model types for enhanced inference speed and power efficiency.

In direct comparison to the Apple M4 Neural Engine, Google's Tensor G4 combines TPU capabilities with broader CPU and GPU integration, but the TPU portion is generally considered specialized for Google's internal AI workloads. Benchmarks show the Apple M4's AI performance surpassing Google Tensor G4's AI performance in neural processing tasks within devices, giving Apple a considerable edge in raw AI performance at the chip level for device-embedded AI.

Developer Ecosystem and Accessibility

Apple's Neural Engine is fully accessible to all developers through its Core ML framework, allowing optimized AI model deployments across iOS, macOS, and iPadOS applications. Apple's ecosystem encourages developers to leverage the Neural Engine for neural network acceleration, providing a broad suite of tools for model conversion, quantization, and performance tuning. This ecosystem support maximizes the utilization of the Neural Engine by third-party apps, making it a versatile AI accelerator.

In contrast, Google TPU technology in consumer Tensor chips is more closely integrated with Google's software stack, primarily targeting AI tasks within Google services and applications such as Google Assistant, Pixel camera features, and voice recognition. While there is support for ML on Android through Android Neural Networks API (NNAPI), optimization for Google TPU specifically is less accessible to third-party developers compared to Apple's Neural Engine. Developers targeting Google TPUs often work within constraints tied to Google's AI ecosystem, and optimizing AI models across different mobile NPUs—from Qualcomm to Samsung—requires distinct tuning efforts.

Use Cases and Application Domains

The Apple M4 Neural Engine powers a variety of AI features on Apple devices, including advanced photography and video processing, natural language understanding, on-device speech recognition, and real-time augmented reality experiences. Its tight integration with Apple's other silicon components enables seamless and efficient AI acceleration that benefits overall user experience and battery life.

Google's TPU technology, while influential in its cloud infrastructure, in its consumer device form focuses on enhancing AI features such as speech recognition, camera image processing, and personalized assistant functions. TPU accelerators in devices like Google Pixel phones enhance specific AI-driven functions rather than general-purpose machine learning tasks. These TPUs are tailored to Google's AI model architecture and deployment strategies.

Efficiency and Power Consumption

The Apple M4 chip, including its Neural Engine, operates within a 20W thermal design power (TDP) envelope, targeting laptops and high-end tablets, showcasing a balance of performance and efficiency enabled by the 3 nm TSMC process. The high TOPS performance of the Neural Engine at this power level highlights Apple's focus on delivering substantial AI capabilities without excessive power draw, essential for mobile computing.

Google's Tensor G4 chip is designed within about a 10W TDP for smartphones and edge devices. Its 4 nm process helps power efficiency despite including TPU accelerators. However, the overall AI processing power is generally less than that of Apple's M4 Neural Engine, reflecting different design priorities between a mobile smartphone SoC and a larger mobile-compute chip like the M4.

Summary of Differences

The Apple M4's Neural Engine stands out with higher raw AI performance (38 TOPS), broader developer accessibility via Core ML, and use in premium devices like the MacBook and iPad series. Google's TPU cores embedded in the Tensor G4 chip offer efficient AI acceleration focused on Android smartphones and specific Google AI services, optimized more narrowly for Google's ecosystem and TensorFlow workloads.