tag: fixed point

Data Formats For Inference On The Edge
By Brian Bailey - 14 Dec, 2023 - Comments: 0

AI/ML training traditionally has been performed using floating point data formats, primarily because that is what was available. But this usually isn't a viable option for inference on the edge, where more compact data formats are needed to reduce area and power. Compact data formats use less space, which is important in edge devices, but the bigger concern is the power needed to move around... » read more

Challenges For New AI Processor Architectures
By Brian Bailey - 29 Jul, 2021 - Comments: 0

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

Reducing Latency, Power, and Gate Count with Floating-Point FMA
By Cadence - 12 Dec, 2018 - Comments: 0

Today’s digital signal processing applications such as radar, echo cancellation, and image processing are demanding more dynamic range and computation accuracy. Floating-point arithmetic units offer better precision, higher dynamic range, and shorter development cycles when compared with fixed-point arithmetic units. Minimizing the design’s time to market is more important than ever. Algori... » read more

Implementing Mathematical Algorithms In Hardware For Artificial Intelligence
By John Swanson - 25 Oct, 2018 - Comments: 0

Petabytes of data efficiently travels between edge devices and data centers for processing and computing of AI functions. Accurate and optimized hardware implementations of functions offload many operations that the processing unit would have to execute. As the mathematical algorithms used in AI-based systems evolve, and in some cases stabilize, the demand to implement them in hardware increase... » read more

Machine Learning’s Growing Divide
By Brian Bailey - 11 Jan, 2018 - Comments: 7

[getkc id="305" kc_name="Machine learning"] is one of the hottest areas of development, but most of the attention so far has focused on the cloud, algorithms and GPUs. For the semiconductor industry, the real opportunity is in optimizing and packaging solutions into usable forms, such as within the automotive industry or for battery-operated consumer or [getkc id="76" kc_name="IoT"] products. ... » read more

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