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Timing Challenges In The Age Of AI Hardware


In recent years, we have seen a clear market trend towards dedicated integrated circuits (ASICs) that are much more efficient in performance and energy consumption than traditional general-purpose computers for processing AI workloads. These AI accelerators harden deep learning algorithm kernels into circuits, enable higher data ingestion bandwidth with local memory, and perform massively paral... » read more

Addressing Power Challenges In AI Hardware


Artificial intelligence (AI) accelerators are essential for tackling AI workloads like neural networks. These high-performance parallel computation machines provide the processing efficiency that such high data volumes demand. With AI playing increasingly larger roles in our lives—from consumer devices like smart speakers to industrial applications like automated factories—it’s paramount ... » read more

Tapping Into Purpose-Built Neural Network Models For Even Bigger Efficiency Gains


Neural networks can be categorized as a set of algorithms modelled loosely after the human brain that can ‘learn’ by incorporating new data. Indeed, many benefits can be derived from developing purpose-built “computationally efficient” neural network models. However, to ensure your model is effective, there are several key requirements that need to be considered. One critical conside... » read more

Convolutional Neural Network With INT4 Optimization


Xilinx provides an INT8 AI inference accelerator on Xilinx hardware platforms — Deep Learning Processor Unit (XDPU). However, in some resource-limited, high-performance and low-latency scenarios (such as the resource-power-sensitive edge side and low-latency ADAS scenario), low bit quantization of neural networks is required to achieve lower power consumption and higher performance than provi... » read more

The Benefits Of Using Embedded Sensing Fabrics In AI Devices


AI chips, regardless of the application, are not regular ASICs and tend to be very large, this essentially means that AI chips are reaching the reticle limits in-terms of their size. They are also usually dominated by an array of regular structures and this helps to mitigate yield issues by building in tolerance to defect density due to the sheer number of processor blocks. The reason behind... » read more

ResNet-50 Does Not Predict Inference Throughput For MegaPixel Neural Network Models


Customers are considering applications for AI inference and want to evaluate multiple inference accelerators. As we discussed last month, TOPS do NOT correlate with inference throughput and you should use real neural network models to benchmark accelerators. So is ResNet-50 a good benchmark for evaluating relative performance of inference accelerators? If your application is going to p... » read more

AI & IP In Edge Computing For Faster 5G And The IoT


Edge computing, which is the concept of processing and analyzing data in servers closer to the applications they serve, is growing in popularity and opening new markets for established telecom providers, semiconductor startups, and new software ecosystems. It’s brilliant how technology has come together over the last several decades to enable this new space starting with Big Data and the idea... » read more

The Emergence Of Hardware As A Key Enabler For The Age Of Artificial Intelligence


Over the past few decades, software has been the engine of innovation for countless applications. From PCs to mobile phones, well-defined hardware platforms and instruction set architectures (ISA) have enabled many important advancements across vertical markets. The emergence of abundant-data computing is changing the software-hardware balance in a dramatic way. Diverse AI applications in fa... » read more

Interconnect Challenges Grow, Tools Lag


Interconnects are becoming much problematic as devices shrink and the amount of data being moved around a system continues to rise. This limitation has shown up several times in the past, and it's happening again today. But when the interconnect becomes an issue, it cannot be solved in the same way issues are solved for other aspects of a chip. Typically it results in disruption in how the t... » read more

ML, Edge Drive IP To Outperform Broader Chip Market


The market for third-party semiconductor IP is surging, spurred by the need for more specific capabilities across a wide variety of markets. While the IP industry is not immune to steep market declines in semiconductor industry, it does have more built-in resilience than other parts of the industry. Case in point: The top 15 semiconductor suppliers were hit with an 18% decline in 2019 first-... » read more

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