I am Sam I am Sam Sam I am That Sam-I-am! That Sam-I-am! I do not like that Sam-I-am! Do you like green eggs and ham? I do not like them, Sam-I-am. I do not like green eggs and ham. I would not like them here or there. I do not like them anywhere. I do not like green eggs and ham. I do not like them,Sam I am.
By its cofounder's reckoning, Efinix is in the right place at the right time. Engineers are struggling to squeeze AI, and especially its deep learning variant, into chips where cost and power are real constraints. The startup, based in Santa Clara. Calif., plans to deliver a new kind of field-programmable gate array (FPGA) technology that is about one-quarter the size of comparable chips, consumes half the power, and is considerably less complex to construct. That combination-- what Efinix calls Quantum programmable technology -- could help push deep learning and AI in general away from central computers and servers and out toward where the data they work on is being generated, according to cofounder, president, and CEO Sammy Cheung. FPGA's having been using the same basic architecture for decades. From a highlevel, it looks like a checkerboard, with alternating sections that are dedicated to either routing or logic. Cheung and cofounder, FPGA-expert Tony Ngai, dedicated function, each square-- called an eXchangeable Logic and Routing cell (XLR)- can be programmed to perform either purpose.
It seems like there's a touchscreen on everything these days: your phone, the checkout kiosk at the grocery store, your car. Sure, they're handy. You can input a PIN, a password, or even sign your name. But maybe somethings just shouldn't have them. Before you mar your nice wooden door with a touchpad controlled lock, consider what engineers at Rutgers University have done. Led by Yingying Chen, a professor of electrical and computer engineering at Rutgers, they created a biometric access system, called VibWrite, that should work on any solid surface -- no touchpad or fingerprint sensor required. The system reads the change that finger pressure and motions make in the vibrations on say a door or a tabletop or, Chen expects, any other solid object. When tested on a wood door and a wood table VibWrite verified identities with greater that 95 percent accuracy and with a false-positive rate of less than 3 percent. Chen delivered the details of the technology this week at the ACM Converence on Computer and Communications Security, in Dallas, Tex.
No lines are longer than 80 characters, TYVM. Other specified properties aren't being scored automatically at this time so this is not necessarily good news...