Tachyum released a white paper that examines how its Prodigy processor addresses trends in AI, enabling deep learning workloads that are environmentally friendly by achieving less energy consumption and carbon emissions. The AI training models leveraged are all fully functional, ensuring Prodigy remains on the leading edge of the industry.
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“With our recently released architectural white paper detailing the Prodigy processor, we have received tremendous interest and were asked for more details about our AI features”
Prodigy, the world’s first Universal Processor, delivers a revolutionary new architecture that unifies the functionality of CPU, GPGPU, and TPU into a single monolithic device, enabling Prodigy processors to address the high demands of cloud and HPC/AI workloads, without expensive and power-hungry accelerators, by using a simple homogenous software model that is aligned with software composability and dynamic reallocation of server resources to maximize utilization.
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Among the features described in the latest Tachyum white paper are AI training techniques that include:
- Quantization Aware Training – Used with low-precision data types for training deep neural network models. Prodigy processors allow deep neural network training using just 8-bit floating point (FP8) representations for all matrix and tensor computations – weights, activations, errors, and gradients. This leads to dramatic improvements in computational efficiency and energy efficiency without impacting model convergence and accuracy, lessening memory transfer times and enabling the use of fast low-precision computing operations.
- Per-Layer Gradient Scaling – Tachyum employs a per-layer gradient scale instead of a global loss scale for the training of deep neural networks using FP8.
- Sparsity – Tachyum’s Prodigy Processor provides native support for block structure sparsity and super-sparsity. It supports sparse matrix multiplication optimized for compressed neural network models, thus reducing memory and computation requirements.
- Results for Image Classification and Instance Segmentation – the company trains a wide range of computer vision models, object detection and instance segmentation models using the FP8 format.
“With our recently released architectural white paper detailing the Prodigy processor, we have received tremendous interest and were asked for more details about our AI features,” said Dr. Radoslav Danilak, founder and CEO of Tachyum. “This latest deep dive provides insight into all the AI training models that can run on Prodigy and how each of those approaches allows system developers to tackle increasingly complex deep learning calculations, delivering an order of magnitude better performance than is currently available from competitors.”
Prodigy delivers unprecedented data center performance, power, and economics, reducing CAPEX and OPEX significantly. Because of its utility for both high-performance and line-of-business applications, Prodigy-powered data center servers can seamlessly and dynamically switch between workloads, eliminating the need for expensive dedicated AI hardware and dramatically increasing server utilization. Tachyum’s Prodigy delivers performance up to 4x that of the highest performing x86 processors (for cloud workloads) and up to 3x that of the highest performing GPU for HPC and 6x for AI applications.
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