Gowin Semiconductor Embeds 64Mb HyperRAM DRAM from Winbond for AI Edge Computing Solution

By Tiera Oliver

Associate Editor

Embedded Computing Design

January 18, 2021

News

Winbond Electronics announced that FPGA manufacturer Gowin Semiconductor has embedded a Winbond 64Mb HyperRAM fast memory device in its new GoAI 2.0 machine learning platform. 

Winbond Electronics announced that FPGA manufacturer Gowin Semiconductor has embedded a Winbond 64Mb HyperRAM fast memory device in its new GoAI 2.0 machine learning platform. 

GoAI 2.0 is a complete, new hardware and software solution for machine learning applications. Compatible with the TensorFlow machine learning development environment, GoAI 2.0 is aimed at edge computing applications such as smart door locks, smart speakers, voice-activated devices, and smart toys. 

The hardware component of the GoAI 2.0 platform, the GW1NSR4, is a system-in-package (SiP) featuring a FPGA and ARM Cortex M3 microcontroller for the machine learning application supported by Winbond’s 64Mb HyperRAM supplied in known good die (KGD) format. 

Per the company, the Winbond DRAM based on HyperRAM technology is ideal for Gowin’s target applications, in which the electronics circuit needs to be made as small as possible, while providing storage and data bandwidth to support compute-intensive workloads such as keyword detection or image recognition. Also, per the company, Winbond’s 64Mb HyperRAM product has connected 11 signal pins, so its connections to the host FPGA is minimal. The GW1NSR4 SiP has a footprint of 4.2mm x 4.2mm in a BGA package. The 64Mb memory capacity provided by the Winbond device is sufficient to run both an operating system and to concurrently operate as buffer memory for a TinyML model, or as a frame buffer. 

The performance specifications of the Winbond’s 64Mb HyperRAMinclude maximum data bandwidth of 500MB/s. It also offers ultra-low power consumption in operating and hybrid sleep modes. 

Winbond’s HyperRAM products are available for high-volume production in densities of 512Mb, 256Mb, 128Mb, 64Mb and 32Mb.

For more information, visit: www.winbond.com

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Tiera Oliver, Associate Editor for Embedded Computing Design, is responsible for web content edits, product news, and constructing stories. She also assists with newsletter updates as well as contributing and editing content for ECD podcasts and the ECD YouTube channel. Before working at ECD, Tiera graduated from Northern Arizona University where she received her B.S. in journalism and political science and worked as a news reporter for the university’s student led newspaper, The Lumberjack.

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