Reducing Latency, Power, and Gate Count with Floating-Point FMA

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

Week In Review: Design, Low Power

Tools & IP UltraSoC debuted functional safety-focused Lockstep Monitor, a set of configurable IP blocks that are protocol aware and can be used to cross-check outputs, bus transactions, code execution, and register states between two or more redundant systems. It supports all common lockstep / redundancy architectures, including full dual-redundant lockstep, split/lock, master/checker, and... » read more

Formal Datapath Verification

J.T. Longino, formal verification application engineer at Synopsys, drills down into how to achieve confidence in datapath designs by applying formal solvers and methods to data transformation areas of a design rather than the control path areas.     See other tech talk videos here. » read more

Implementing Mathematical Algorithms In Hardware For Artificial Intelligence

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

AI Chips Must Get The Floating-Point Math Right

Most AI chips and hardware accelerators that power machine learning (ML) and deep learning (DL) applications include floating-point units (FPUs). Algorithms used in neural networks today are often based on operations that use multiplication and addition of floating-point values, which subsequently need to be scaled to different sizes and for different needs. Modern FPGAs such as Intel Arria-10 ... » read more

Machine Learning’s Growing Divide

[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

Achieving Numerical Precision And Design Customization With Flexible Floating-Point IP

Floating-point operations in application-specific hardware have gained in popularity mostly because they are easier to use than fixed-point operations and they are a better match to numerical behavior in software algorithms. Fixed-point operations present design challenges in the definition of input/output ranges and internal precision for each operation. On the other hand, floating-point opera... » read more