Renesas announces new MCU group targeting AI and ML applications
Renesas has introduced the RA8P1 microcontroller (MCU) Group targeted at Artificial Intelligence (AI) and Machine Learning (ML) applications, as well as real-time analytics. The new MCUs establish a new performance level for MCUs by combining 1GHz Arm Cortex-M85 and 250MHz Cortex-M33 CPU cores with the Arm Ethos-U55 Neural Processing Unit (NPU). This combination delivers the highest CPU performance of over 7300 CoreMarks and AI performance of 256 GOPS at 500 MHz.
The RA8P1 is optimised for edge and endpoint AI applications, using the Ethos-U55 NPU to offload the CPU for compute intensive operations in Convolutional and Recurrent Neural Networks (CNNs and RNNs) to deliver up to 256 MACs per cycle that yield 256 GOPS performance at 500 MHz. The new NPU supports most commonly used networks, including DS-CNN, ResNet, Mobilenet TinyYolo and more. Depending on the neural network used, the Ethos-U55 provides up to 35x more inferences per second than the Cortex-M85 processor on its own.
The RA8P1 MCUs are manufactured on the 22ULL (22nm ultra-low leakage) process from TSMC, enabling ultra-high performance with very low power consumption. This process also enables the use of embedded Magnetoresistive RAM (MRAM) in the new MCUs. MRAM offers faster write speeds along with higher endurance and retention compared with Flash.
Renesas has integrated dedicated peripherals, ample memory and advanced security to address Voice and Vision AI and Real-time Analytics applications. For vision AI, a 16-bit camera interface (CEU) is included that supports sensors up to 5 megapixels, enabling camera and demanding Vision AI applications. A separate MIPI CSI-2 interface offers a low pin-count interface with two lanes, each up to 720Mbps. In addition, multiple audio interfaces including I2S and PDM support microphone inputs for voice AI applications.
The RA8P1 offers both on-chip and external memory options for efficient, low latency neural network processing. The MCU includes 2MB SRAM for storing intermediate activations or graphics framebuffers. 1MB of on-chip MRAM is also available for application code and storage of model weights or graphics assets. High-speed external memory interfaces are available for larger models. SIP options with 4 or 8 MB of external flash in a single package are also available for more demanding AI applications.
Along with the RA8P1 MCUs, Renesas has introduced RUHMI (Renesas Unified Heterogenous Model Integration), a comprehensive framework for MCUs and MPUs. RUHMI offers efficient AI deployment of the latest neural network models in a framework agnostic manner. It enables model optimisation, quantisation, graph compilation and conversion, and generates efficient source code. RUHMI provides native support for machine-learning AI frameworks such as TensorFlow Lite, Pytorch & ONNX. It also provides the necessary tools, APIs, code-generator, and runtime needed to deploy a pre-trained neural network, including ready-to-use application examples and models optimised for RA8P1. RUHMI is integrated with Renesas’s own e2 studio IDE to allow seamless AI development. This integration will facilitate a common development platform for MCUs and MPUs.
The RA8P1 MCUs provide leading-edge security for critical applications. The new Renesas Security IP (RSIP-E50D) includes numerous cryptographic accelerators, including CHACHA20, Ed25519, NIST ECC curves up to 521 bits, enhanced RSA up to 4K, SHA2 and SHA3. In concert with Arm TrustZone®, this provides a comprehensive and fully integrated secure element-like functionality. The new MCUs also provides strong hardware Root-of-Trust and Secure Boot with First Stage Bootloader (FSBL) in immutable storage. XSPI interfaces with decryption-on-the-fly (DOTF) allow encrypted code images to be stored in external flash and decrypted on the fly as it is securely transferred to the MCU for execution.
