Gowin Semiconductor Corp., San Jose, Calif. and Guanzhou, China, releases the latest version of their GoAI machine learning platform, providing an SDK and accelerator to perform machine learning for edge inference using convolutional neural networks on Gowin field-programmable gate arrays (FPGAs).<\/p>\n
Gowin GoAI 2.0 offers direct integration into the TensorFlow and TensorFlow Lite Machine Learning Platforms, optimization for targeting Gowin\u2019s GW1NSR4P \u00b5SoC FPGA, and an accelerator to offload compute-intensive functions from the microcontroller embedded within Gowin FPGAs with additional 80x performance.<\/p>\n
Machine learning is a rapidly developing field and development is aligning on frameworks, platforms, models, and datasets for better standardization, reliability, and ease of development. TensorFlow is an aligning platform that includes support for embedded SoC\u2019s and microcontrollers. GoAI 2.0 adds the capacity to easily use TensorFlow with embedded FPGAs from Gowin.<\/p>\n
With GoAI 2.0 no FPGA RTL or microprocessor C\/C++ programming is required. The C\/C++ code to drive the accelerator from the ARM Cortex-M processor is generated automatically by the GoAI 2.0 SDK. The GoAI 2.0 accelerator is offered as an FPGA IP, but is also included as part of pre-generated FPGA bitstreams with various of sensor inputs. The GoAI 2.0 accelerator architecture is designed such that only a register map needs to be updated per model layer by the processor and no RTL changes are required to deploy or change the machine learning model used.<\/p>\n
While machine learning can have a significant learning curve, GoAI 2.0 offers several starting reference designs for sensor inputs such as cameras, microphones, and accelerometers. These include data inference of predicting a sine wave output based on an input value, audio phrase detection for inferring a phrase of \u201cyes\u201d or \u201cno\u201d from a microphone input, and person detection inferring the presence of a human in view of a camera. Additional designs being developed include gesture detection to detect the shape a user draws in the air while holding the development board using an accelerometer.<\/p>\n
Developers can get started with GoAI 2.0 using one of three GoAI Embedded Development Kits. The development kits include various sizes of GOWIN Gowin FPGAs, sensors to run the reference designs, and other peripherals such as HDMI inputs and outputs for video demonstrations.<\/p>\n