Deep learning inference performance on the Yitian 710
In recent years, deep learning has been widely implemented in various areas of industry, such as vision, natural language processing, and recommender systems. The exponential rise in the number of deep learning model parameters and the new business demand for complex models require cloud vendors to reduce arithmetic costs and improve computational efficiency. This condition is especially true in deep learning inference, which has become our focus for optimization. Under this influence, Alibaba Cloud unveils the new Arm server chip - Yitian 710, with the 5nm process. Yitian 710 is based on Arm Neoverse and supports the latest Armv9 instruction set. This instruction set includes extended instruction such as Int8 MatMul, BFloat16 (BF16), and others, enabling a performance advantage in high-performance computing.
In this blog post, we focus on Alibaba Elastic Cloud Service (ECS) powered by Yitian 710 to test and compare the performance of deep learning inference.
To read the full article, click here
Related Semiconductor IP
- CAN XL Verification IP
- Rad-Hard GPIO, ODIO & LVDS in SkyWater 90nm
- 1.22V/1uA Reference voltage and current source
- 1.2V SLVS Transceiver in UMC 110nm
- Neuromorphic Processor IP
Related Blogs
- Improve Apache httpd Performance up to 40% by deploying on Alibaba Cloud Yitian 710 instances
- The CEVA-XM6 Vision Processor Core Boosts Performance for Embedded Deep Learning Applications
- AImotive Expands Into Silicon IP for Deep Learning Inference Acceleration
- Scaling Out Deep Learning (DL) Inference and Training: Addressing Bottlenecks with Storage, Networking with RISC-V CPUs
Latest Blogs
- Analog Design and Layout Migration automation in the AI era
- UWB, Digital Keys, and the Quest for Greater Range
- Building Smarter, Faster: How Arm Compute Subsystems Accelerate the Future of Chip Design
- MIPS P8700 RISC-V Processor for Advanced Functional Safety Systems
- Boost SoC Flexibility: 4 Design Tips for Memory Subsystems with Combo DDR3/4 Interfaces