YPOLOGIST - Capabilities Statement
Ypologist's XPU is a massively parallel novel architecture targeting
Deep Learning and Neural Network applications.
XPU's many-core FPGA and ASIC
implementations will deliver excellent cost and power-efficient alternatives to
accelerators such as Nvidia's GPCPU or Intel's MIC.
The main application domains, for which libraries will be developed, are linear
algebra, cryptocurrencies, automotive and bio-informatics.
The following are the performance characteristics for each of these applications:
Linear Algebra 2 to 3X improvement in energy savings and
3X increase the ratio performance/peak performance compared with
In most Big-Data applications power consumption per task is 20%.
Cryptocurrency applications request high MH/sec/Watt (MH stands for mega hashes).
Measured performance 337% against NVIDIA GPU.
In automotive stereo vision, where tight real-time performance and temperature
requirements are mandatory, XPU's 1024-cell system consumes less than 6W at
Bio-Informatics applications are accelerated 6X when implemented in a 28nm
FPGA when compared with a 22nm 4-core Intel CPU with SSE. Simulations predict
a 30X improvement. The energy efficiency is improved 20X for the
XPU FPGA version and 300X for the XPU ASIC version.