EFinix
Canvas Category Hardware : Edge Computing : FPGA
Co-founders Sammy Cheung and Tony Ngai formed EFinix in 2012 after spending over 25 years working at other semiconductor companies, including FPGA companies and other programmable providers. They began by inventing a unique, disruptive, flexible FPGA fabric, the Quantum™ architecture. Quantum is Efinix’s key technology: an FPGA fabric arranged in a simple block-based format with interspersed columns of RAM and multipliers or DSP blocks. This fabric is designed to be process and fab agnostic, as well as compatible with a true SoC design flow.
Assembly Line
Economics of the FPGA
Successive generations of chips are becoming harder to justify where smaller process geometries become nonlinearly more expensive. An increased integration however, drives up packaging complexity and cost. This increase in complexity grows design time and expense. Because of these factors, the margins with which to gain profit from the volume production of ICs are reducing. First, the increase in competition allows for more consumer choices, which reduces per-product volumes. This increase in competition also reduces the life duration and lifetime volume of products. New compute-intensive nodes and technologies must be increasingly agile, not only to support the changing market demands but also to keep up with upgrading deep-learning models. There is an apparent lack of FPGAs being used for AI applications that sit in the “middle of the road” in terms of complexity, causing designers to rely either on custom chiplets or embedded processors for hardware acceleration. The new FPGA economy paradigm opened by Efinix frees designers to more flexibly innovate in a realm that will deliver revolutionary benefits to society. This once-in-a-lifetime quantum shift in product design possibilities is providing an inflection point away from the dead end of custom silicon and into the customizable blank slate of FPGA fabric.
FPGA comes back into its own as edge computing and AI catch fire
The niche of edge computing burdens devices with the need for extremely low power operation, tight form factors, agility in the face of changing data sets, and the ability to evolve with changing AI capabilities via remote upgradeability — all at a reasonable price point. This is, in fact, the natural domain of the FPGA with an inherent excellence in accelerating compute-intensive tasks in a flexible, hardware-customizable platform. However, much of the available off-the-shelf FPGAs are geared toward data center applications in which power and cost profiles justify the bloat in FPGA technologies.