AI on a silicon
One More Thing
An exciting week for Apple fans!
In its "One More Thing" event, Apple announces the new lineup of Macs, which include the MacBook Air, Mac mini, and the MacBook Pro. Powering these computers is an Apple-designed M1 chip. Based on the ARM architecture, the M1 chip is an SoC ( System On A Chip ) that combines numerous powerful technologies ( 8-core CPU, 8-core GPU, 16-core Neural Engine ) into a single chip. The M1 chip also features a unified memory architecture for dramatically improved performance and efficiency.
The integrated design of M1 chip represents a shift from the traditional Mac's Intel-based architecture. And among M1's many powerful technologies, the Neural Engine stands out. With dedicated neural network hardware, it excels at the most demanding machine learning tasks such as video analysis, voice recognition, and image processing. Such dedicated design enables Apple to implement neural networks and machine learning in a more energy-efficient manner than using either the main CPU or the GPU.
With all these great features, I can't wait to get to own one of these powerful Macs
AMD acquiring Xilinx
I want to congratulate Good AI's LP ( Limited Partner ) Jack Balletto for the recent AMD's 35B acquisition of Xilinx. A Silicon Valley semiconductor icon ( founding engineer at Fairchild Semiconductor, CEO of VLSI Technologies ), Jack sourced the first round of financing for Xilinx during his distinctive career at Hambrecht & Quist ( H&Q ).
As a leader in the FPGAs ( Field-Programmable Gate Array ) as well as SoCs ( System On a Chip ) for datacenter (including SmartNICs), communications, automotive, industrial, aerospace, and defense markets, Xilinx will help expand AMD's core market in high-performance CPUs/GPUs for PCs and datacenter servers. This marriage will also position the two companies to pursue the ever-growing AI opportunities synergistically.
Specifically, FPGAs, integrated circuits with a programmable hardware fabric, offer a combination of speed, programmability, and flexibility—delivering performance without the cost and complexity of developing custom ASICs ( Application-Specific Integrated Circuits ) or GPUs. With AI Deep Learning's constantly evolving training model, FPGA's programmable hardware can deliver superior performance for AI applications where low latency is critical.
Congratulations to Jack again!