FPGAs aid hardware acceleration for embedded designs

Article By : Susan Nordyk

Fabricated on a 16-nm process node, Efinix Trion Titanium FPGAs incorporate the company’s Quantum fabric for compute acceleration, machine learning, and deep learning.

Efinix Trion Titanium FPGAs are fabricated on a 16-nm process node and incorporate the company’s Quantum fabric for compute acceleration, machine learning, and deep learning. Combined with Efinix RISC-V SoCs, Titanium FPGAs form the compute core and adaptive hardware acceleration for complete embedded system-in-package (SiP) designs.

block diagram for the Efinix Trion Titanium FPGA

Leveraging the Quantum fabric’s enhanced exchangeable logic and routing (XLR) cell and 2X efficiency improvement, along with highly configurable embedded memory blocks and dedicated high-speed DSP blocks, Titanium FPGAs pack plenty of processing power into a die size that is just a quarter of the area of previous-generation Trion devices. The low power consumption of the 16-nm node enables Titanium devices to consume a third of the power of Trion FPGAs and overcome all of the thermal issues associated with highly-integrated applications.

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