> AUTOMOTIVE-FEATURE
High Reliable And Performance Deep Learning Accelerator for ADAS and Autonomous Driving Systems
Katsushige Matsubara
Sr. Manager, Automotive Solution Business Unit Renesas Electronics Corporation
Next-generation ADAS and autonomous driving (AD) systems, when deployed to market, will require accurate and highspeed recognition, judgment, and operation. Renesas presented these achievements at International SolidState Circuits Conference 2021 (ISSCC 2021), which take place February 13 to 22, 2021. We will continue to develop and deploy in-vehicle LSI based on this technology. We expect these will contribute to the realization of a safe and secure car society through the spread of ADAS and AD systems. Convolutional neural networks (CNNs) require large amounts of computation for pattern recognition. As the number of sensors installed increases, higher CNN performance is required. However, as power consumption increases in proportion to performance, a heavy and expensive water-cooling system is needed. It is required to achieve both high deep learning performance and low power consumption that enables a lightweight and cost-effective air-cooling system. Achieving a CNN performance of 60TOPS with an efficiency of 10TOPS/W per one LSI device is the optimal target from a practical point of view.
> MAY 2021
CNN accelerator with high performance and power efficiency
A CNN accelerator (CNNA) performance/efficiency target is to achieve 60TOPS performance with 10TOPS/W efficiency. From an implementation point of view, it is realized with three identical accelerators instead of one accelerator. One CNNA contains 13,824 MAC arithmetic units and operates at 800MHz. The theoretical maximum performance of the three CNNAs is 66TOPS. In addition, each CNNA connects 2MB dedicated scratchpad memory (SPM) through a 512-bit interconnect module. This increases the execution efficiency of CNNA,
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