FCCM2020: High-Throughput Convolutional Neural Network on an FPGA by Customized JPEG Compression

Challenges faced during embedded system design: The challenges in design of embedded systems have always been in the same limiting requirements for decades: Small form factor; Low energy; Long-term stable performance without maintenance.

ppt : Basic of AI Accelerator Design using Verilog HDL git : https://github.com/matbi86/01_ai_accelerator_basic_for_student ref : http://eyeriss.mit.edu/tutorial.html

The document provides an overview of the ARM architecture and Cortex-M3 processor. It discusses ARM Ltd.'s history and business model as an IP licensing company. It then describes the Cortex-M3 microcontroller, including its programmer's model, exception and interrupt handling, pipeline, and instruction sets. Key points are the Cortex-M3's stack-based exception model, 3-stage pipeline, conditional execution support, and AHB/APB system design integration.

This document describes the design and simulation of different 8-bit multipliers using Verilog code. It summarizes four multipliers: array, Wallace tree, Baugh-Wooley, and Vedic. It finds that the Baugh-Wooley multiplier has advantages in speed, delay, area, complexity, and power consumption compared to the other multipliers. The document also discusses half adders, full adders, ripple carry adders, carry save adders, and multiplication algorithms. It aims to compare the multipliers based on area, speed, and delay.

5G-NR (New Radio) is the 5G wireless standard developed by 3GPP to support both sub-6 GHz and mmWave spectrum. It supports three main use cases - enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). 5G-NR can operate in both non-standalone and standalone modes, with non-standalone relying on the existing 4G LTE network for core functionality and standalone operating independently. Key 5G technologies include higher peak data rates up to 20 Gbps, lower latency around 1 ms, support for high mobility up to 500 km/h, and ability to connect a massive number of devices

The SPI (Serial Peripheral Interface) is a synchronous serial communication protocol used for communication between devices. It uses a master-slave architecture with a single master device initiating data transfer. Key features include using separate clock and data lines, operating in full duplex mode, and allowing multiple slave devices through individual chip selects. It provides a lower pin count solution than parallel buses at the cost of slower communication speeds.

Placement is the process of determining the locations of circuit devices on a chip. It is a critical step that affects performance, routability, heat distribution, and power consumption. There are different types of placement like standard cell placement and building block placement. Placement algorithms aim to optimize objectives like minimizing total area and wire length. Simulated annealing is a commonly used iterative placement algorithm that models the physical annealing process to arrive at a low-cost solution. Other algorithms include partitioning-based approaches and cluster growth.

This document discusses various VLSI testing techniques. It begins by explaining the need for testing circuits when they are first developed and manufactured to check that they meet specifications. The main testing approach is to apply test inputs and compare the outputs to expected patterns. It then describes different testing techniques for combinational and sequential circuits, including fault modeling, path sensitizing, scan path testing, built-in self-test (BIST), boundary scan testing, and signature analysis. Specific circuit examples are provided to illustrate scan path testing, BIST using linear feedback shift registers (LFSRs) and compressor circuits, and boundary scan testing.

This document summarizes the physical layer design of LTE Release 8 and enhancements for LTE-Advanced. It describes the downlink and uplink multiple access schemes, reference signals, control signaling, data transmission procedures, UE categories, and support for frequency division duplex and time division duplex operation. The document provides an overview of the 3GPP release timeline and the specifications that define the LTE physical layer.

This Presentation describes the ARM CORTEX M3 core processor with the details of the core peripherals. Soon a CORTEX base controller(STM32F100RBT6) ppt will be uploaded. For more information mail me at:gaurav.iitkg@gmail.com.

This document provides an overview of C programming for embedded systems. It discusses how embedded programming differs from general programming, focusing on resource constraints, hardware differences, and lack of debugging tools in embedded systems. It also covers how C is commonly used for embedded programming, emphasizing static memory allocation, inline assembly, and avoiding complex features. Finally, it introduces the GCC toolchain for compiling C code for embedded devices.

The 8086 microprocessor is a 16-bit CPU launched by Intel in 1978. It has a 16-bit data bus and 20-bit address bus, allowing it to access up to 1MB of memory. The 8086 architecture partitions the CPU logic into two functional units - the Bus Interface Unit which handles external transactions, and the Execution Unit which performs decoding and execution. This separation improves processing speed by allowing parallel instruction fetching and execution via pipelining. The 8086 uses memory segmentation to access more memory than its 16-bit registers allow, dividing the 1MB address space into 64KB segments addressed using segment and offset registers.

The document discusses the instruction set of the 8086 microprocessor. It describes that the 8086 has over 20,000 instructions that are classified into several categories like data transfer, arithmetic, bit manipulation, program execution transfer, and string instructions. Under each category, it provides details about specific instructions like MOV, ADD, AND, CALL, etc. and explains their functionality and operand usage.

Here we can find the pin diagram of ADC 0808 and how to interface it with 8086 microprocessor using 8255 PPI

This document discusses concurrent programming with real-time operating systems (RTOS). It begins with an overview of RTOS and what they provide to programmers, such as task management, synchronization primitives, and driver packages. It then discusses specific RTOS concepts like tasks, concurrency primitives like semaphores, and common concurrency problems like data races. Examples are given to demonstrate task creation and using semaphores to safely increment a shared variable between tasks. The document concludes with discussing classical concurrency problems like the dining philosophers problem and potential issues that could arise like deadlock or starvation.

Turbo codes are a type of error correcting code that can achieve performance close to the theoretical maximum allowed by Shannon's limit. Turbo codes use an iterative decoding process between two recursive systematic convolutional encoders separated by an interleaver. This iterative decoding allows turbo codes to correct errors very efficiently. Turbo codes are used in applications like deep space communications and mobile phone networks due to their ability to operate reliably at low signal-to-noise ratios.

1. Different tools use different descriptions for power management, making it difficult to verify configurations and keep definitions consistent across the design flow. 2. There is no automation for verifying power management definitions, requiring designers to manually verify thousands of statements. 3. The design hierarchy and syntax varies between tools and between RTL and gate representations, complicating cross-checking. 4. It is challenging to verify power functionality without changing RTL code since power and ground nets are not explicitly captured or simulated.

This document discusses deep learning initiatives at NECSTLab focused on hardware acceleration of convolutional neural networks using FPGAs. It proposes a framework called CNNECST that provides high-level APIs to design CNNs, integrates with machine learning frameworks for training, and generates customized hardware for FPGA implementation through C++ libraries and Vivado. Experimental results show speedups and energy savings for CNNs like LeNet and MNIST on FPGA boards compared to CPU. Challenges and future work include supporting more layer types and reduced precision computations.

Optimize Convolutional Neural Network by optimizing algorithm and improving with parallelization with OpenMP and OpenMPI

Speech recognition is one of the key topics in artificial intelligence, as it is one of the most common forms of communication in humans. Researchers have developed many speech-controlled prosthetic hands in the past decades, utilizing conventional speech recognition systems that use a combination of neural network and hidden Markov model. Recent advancements in general-purpose graphics processing units (GPGPUs) enable intelligent devices to run deep neural networks in real-time. Thus, state-of-the-art speech recognition systems have rapidly shifted from the paradigm of composite subsystems optimization to the paradigm of end-to-end optimization. However, a low-power embedded GPGPU cannot run these speech recognition systems in real-time. In this paper, we show the development of deep convolutional neural networks (CNN) for speech control of prosthetic hands that run in real-time on a NVIDIA Jetson TX2 developer kit. First, the device captures and converts speech into 2D features (like spectrogram). The CNN receives the 2D features and classifies the hand gestures. Finally, the hand gesture classes are sent to the prosthetic hand motion control system. The whole system is written in Python with Keras, a deep learning library that has a TensorFlow backend. Our experiments on the CNN demonstrate the 91% accuracy and 2ms running time of hand gestures (text output) from speech commands, which can be used to control the prosthetic hands in real-time. 2019 First International Conference on Transdisciplinary AI (TransAI), Laguna Hills, California, USA, 2019, pp. 35-42

This document summarizes an adaptive modular approach for mining sensor network data using machine learning techniques. It presents a two-layer architecture that uses an online compression algorithm (PCA) in the first layer to reduce data dimensionality and an adaptive lazy learning algorithm (KNN) in the second layer for prediction and regression tasks. Simulation results on a wave propagation dataset show the approach can handle non-stationarities like concept drift, sensor failures and network changes in an efficient and adaptive manner.