https://k155la3.blog/categories/tensil/ Recent content in tensil on K155LA3 Hugo -- gohugo.io en-us Fri, 29 Jul 2022 00:00:00 +0000 https://k155la3.blog/2022/07/29/getting-tensil-to-run-resnet-at-300-fps-on-zcu104/ Fri, 29 Jul 2022 00:00:00 +0000 https://k155la3.blog/2022/07/29/getting-tensil-to-run-resnet-at-300-fps-on-zcu104/ Introduction Baseline solution Tensil RTL and Vivado implementation ResNet compiled for Tensil Tensil for Vitis embedded applications Dual clock solution Ultra RAM solution Solutions with large local memory Introduction Sometimes the application requires pushing the performance to its limits. In this tutorial we will show how to optimize Tensil running ResNet20 trained on CIFAR for maximum performance. To do this, we will use the powerful ZCU104 board and implement an embedded application to remove the overhead of running Linux OS and PYNQ. https://k155la3.blog/2022/07/01/building-speech-controlled-robot-with-tensil-and-arty-a7-part2/ Fri, 01 Jul 2022 00:00:00 +0000 https://k155la3.blog/2022/07/01/building-speech-controlled-robot-with-tensil-and-arty-a7-part2/ Introduction This is part II of a two-part tutorial in which we will continue to learn how to build a speech controlled robot using Tensil open source machine learning (ML) acceleration framework, Digilent Arty A7-100T FPGA board, and Pololu Romi Chassis. In part I we focused on recognizing speech commands through a microphone. Part II will focus on translating commands into robot behavior and integrating with the Romi chassis. System architecture Let’s start by reviewing the system architecture we introduced in Part I. https://k155la3.blog/2022/06/26/building-speech-controlled-robot-with-tensil-and-arty-a7-part1/ Sun, 26 Jun 2022 00:00:00 +0000 https://k155la3.blog/2022/06/26/building-speech-controlled-robot-with-tensil-and-arty-a7-part1/ Introduction In this two-part tutorial we will learn how to build a speech controlled robot using Tensil open source machine learning (ML) acceleration framework and Digilent Arty A7-100T FPGA board. At the heart of this robot we will use the ML model for speech recognition. We will learn how Tensil framework enables ML inference to be tightly integrated with digital signal processing in a resource constrained environment of a mid-range Xilinx Artix-7 FPGA. https://k155la3.blog/2022/04/04/tensil-tutorial-for-yolo-v4-tiny-on-ultra96-v2/ Mon, 04 Apr 2022 00:00:00 +0000 https://k155la3.blog/2022/04/04/tensil-tutorial-for-yolo-v4-tiny-on-ultra96-v2/ Introduction This tutorial will use Avnet Ultra96 V2 development board and Tensil open-source inference accelerator to show how to run YOLO v4 Tiny–the state-of-the-art ML model for object detection–on FPGA. The YOLO model contains some operations that Tensil does not support. These operations are in the final stage of processing and are not compute-intensive. We will use TensorFlow Lite (TF-Lite) to run them on the CPU to work around this. We will use the PYNQ framework to receive real-time video from a USB webcam and show detected objects on a screen connected to Display Port. https://k155la3.blog/2022/03/13/tensil-tutorial-for-pynq-z1/ Sun, 13 Mar 2022 00:00:00 +0000 https://k155la3.blog/2022/03/13/tensil-tutorial-for-pynq-z1/ Introduction This tutorial will use the PYNQ Z1 development board and Tensil’s open-source inference accelerator to show how to run machine learning (ML) models on FPGA. We will be using ResNet-20 trained on the CIFAR dataset. These steps should work for any supported ML model – currently all the common state-of-the-art convolutional neural networks are supported. Try it with your model! We’ll give detailed end-to-end coverage that is easy to follow. https://k155la3.blog/2022/03/06/tensil-tutorial-for-ultra96-v2/ Sun, 06 Mar 2022 00:00:00 +0000 https://k155la3.blog/2022/03/06/tensil-tutorial-for-ultra96-v2/ Introduction This tutorial will use the Avnet Ultra96 V2 development board and Tensil’s open-source inference accelerator to show how to run machine learning (ML) models on FPGA. We will be using ResNet-20 trained on the CIFAR dataset. These steps should work for any supported ML model – currently all the common state-of-the-art convolutional neural networks are supported. Try it with your model! We’ll give detailed end-to-end coverage that is easy to follow.