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Transforming AI Models For Accelerator Chips


AI is all about speeding up the movement and processing of data. Ali Cheraghi, solution architect at Flex Logix, talks about why floating point data needs to be converted into integer point data, how that impacts power and performance, and how different approaches in quantization play into this formula. » read more

Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data


Abstract "In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a bro... » read more

Getting Better Edge Performance & Efficiency From Acceleration-Aware ML Model Design


The advent of machine learning techniques has benefited greatly from the use of acceleration technology such as GPUs, TPUs and FPGAs. Indeed, without the use of acceleration technology, it’s likely that machine learning would have remained in the province of academia and not had the impact that it is having in our world today. Clearly, machine learning has become an important tool for solving... » read more

Impact Of GAA Transistors At 3/2nm


The chip industry is poised for another change in transistor structure as gate-all-around (GAA) FETs replace finFETs at 3nm and below, creating a new set of challenges for design teams that will need to be fully understood and addressed. GAA FETs are considered an evolutionary step from finFETs, but the impact on design flows and tools is still expected to be significant. GAA FETs will offer... » read more

Challenges For New AI Processor Architectures


Investment money is flooding into the development of new AI processors for the data center, but the problems here are unique, the results are unpredictable, and the competition has deep pockets and very sticky products. The biggest issue may be insufficient data about the end market. When designing a new AI processor, every design team has to answer one fundamental question — how much flex... » read more

Challenges Of Edge AI Inference


Bringing convolutional neural networks (CNNs) to your industry—whether it be medical imaging, robotics, or some other vision application entirely—has the potential to enable new functionalities and reduce the compute requirements for existing workloads. This is because a single CNN can replace more computationally expensive image processing, denoising, and object detection algorithms. Howev... » read more

11 Ways To Reduce AI Energy Consumption


As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

Fast, Low-Power Inferencing


Power and performance are often thought of as opposing goals, opposite sides of the same coin if you will. A system can be run really fast, but it will burn a lot of power. Ease up on the accelerator and power consumption goes down, but so does performance. Optimizing for both power and performance is challenging. Inferencing algorithms for Convolutional Neural Networks (CNN) are compute int... » read more

New Ways To Optimize Machine Learning


As more designers employ machine learning (ML) in their systems, they’re moving from simply getting the application to work to optimizing the power and performance of their implementations. Some techniques are available today. Others will take time to percolate through the design flow and tools before they become readily available to mainstream designers. Any new technology follows a basic... » read more

Analog: Avoid Or Embrace?


We live in an analog world, but digital processing has proven quicker, cheaper and easier. Moving digital data around is only possible while the physics of wires can be safely abstracted away enough to provide reliable communications. As soon as a signal passes off-chip, the analog domain reasserts control for modern systems. Each of those transitions requires a data converter. The usage ... » read more

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