Flashless MCUs are enabling the smooth operations of high-bandwidth embedded AI systems.
FREMONT, CA: Embedded systems are constantly leveraging artificial intelligence (AI) techniques for applications that include speech recognition, image analysis, and predictive decision-making. Although the incorporation of AI into embedded systems offer improved performance, the systems also require a massive amount of storage. The data utilized and generated during AI computations exceed the capacity that can be supported by the limited embedded memory present in most MCUs. Further, the processing performance of the AI applications competes with the requirement for lower power consumption. These challenges can be addressed through an external flash-memory architecture that offers lower-power, high-throughput memory to allow AI applications to operate in resource-scarce embedded systems.
The increased usage of AI at the edge of the embedded systems further needs additional storage capability. The new applications, such as AI at the edge, can be addressed with the help of a new class of microcontrollers (MCUs). MCUs combine high-performance processing with an MCU’s power efficiency. The use of external flash along with these MCUs can eliminate any practical limit from the size of the memory needed for supporting computations in embedded devices.
The use of serial peripheral interface (SPI) connecting MCU to the memory results in a greater reduction of pin count and power consumption than via any other memory technologies. The use of a low-power, a high-throughput external memory device can seamlessly fetch AI data from an embedded system. However, the modern process nodes may not support on-chip flash. The above issue is getting addressed with the incorporation of EcoXiP, which enables the use of MCUs based on current process nodes that don't support on-chip flash. Thus, EcoXiP results in significant gains in power and performance efficiency.
Thus, the new flashless memory technologies enable lower power consumption and offer better power efficiency when incorporated into embedded systems.
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