Dennard scaling is gone, Amdahl's Law is reaching its limit, and Moore's Law is becoming difficult and expensive to follow, particularly as power and performance benefits diminish. And while none of that has reduced opportunities for much faster, lower-power chips, it has significantly shifted the dynamics for their design and manufacturing. Rather than just different process nodes and half nodes, companies developing chips -- traditional chip companies, automotive OEMs, fabless and non-fabless IDMs, and large systems companies -- are now wrestling with more options and more unique challenges as they seek optimal solutions for their specific applications. And they are all demanding more from an EDA ecosystem, which is racing to keep up with these changes, including various types of advanced packaging, chiplets, and a demand for integrated and customized hardware and software. "While heterogeneous integration predates the end of Dennard scaling or flattening of Moore's Law by several years, silicon designers and system architects are embracing this paradigm now to retain their pursuit of PPA goals -- without empirical law and its derivatives," said Saugat Sen, vice president of R&D at Cadence. "While there are many architectural and design challenges in this era, addressing thermal concerns rise to the top. Efficiency in design and implementation has been intricately linked to closed-loop integration with multi-physics analyses for awhile. More-than-Moore has created a compelling case for the implementation-analyses microcosm to transcend across the fabrics of system design, from silicon to package, and even beyond, and more so in the systems companies that are at the bleeding edge of design innovation."

From the Beatles Let It Be to John Coltrane's A Love Supreme and Radiohead's OK Computer, record labels often remaster classic albums from the greats in many genres of music. These higher fidelity remasters are a welcome treat for aficionados and mainstream fans alike. But what if I told you, just like Led Zeppelin's or Van Halen's greatest hits, semiconductor chips could be "remastered" as well, and these remasters could help bail us out of the current chip shortage? For some folks that would totally rock, pun intended, but let's take a step back and look at the problem and potential solutions at hand first. The process of designing and verifying chips like the modern processors, controllers and sensors in cars, for example, can take years and require millions of dollars of R&D.

This paper and accompanying Python and C++ Framework is the product of the authors perceived problems with narrow (Discrimination based) AI. (Artificial Intelligence) The Framework attempts to develop a genetic transfer of experience through potential structural expressions using a common regulation/exchange value (energy) to create a model whereby neural architecture and all unit processes are co-dependently developed by genetic and real time signal processing influences; successful routes are defined by stability of the spike distribution per epoch which is influenced by genetically encoded morphological development biases.These principles are aimed towards creating a diverse and robust network that is capable of adapting to general tasks by training within a simulation designed for transfer learning to other mediums at scale.

Until now, most processor chips used for AI applications have been adaptations of devices, like GPUs, that were initially developed for other purposes. This is partly because those existing devices have proven effective enough to be useful, and partly because most companies have seen development of AI-specific processors as prohibitively costly, complex, and/or risky. But, with the AI market poised for intense expansion, diverse new applications at the edge and in endpoints will require silicon that's more closely tailored for specific tasks and situations, removing the need to go to the cloud for AI processing. We are now starting to see indications of how the semiconductor sector is evolving to meet those needs, and what it will mean for the evolution of the AI marketplace and those who seek to enter it. A useful case study is provided by Edge AI startup Blaize, which is pursuing customers in industrial, smart city, automotive sensor fusion, last-mile delivery, and retail applications with its recently launched Pathfinder and Xplorer system-on-module platforms and accompanying software tools.

Putting together bits of information dropped during AMD's PC-heavy hour-and-a-half presentation, it becomes apparent that Vega 7nm is finally aimed at high performance deep learning (DL) and machine learning (ML) applications – artificial intelligence (AI), in other words. AMD's EPYC successes may be paving the way for Vega 7nm in cloud AI training and inference applications. AMD claims that the 7nm process node it has co-developed with its fab partners will yield twice the transistor density, twice the power efficiency and about a third more performance than its 14nm process node. An educated guess says that not all Vega 7nm products will sport this high-end memory configuration – I think that showing off 32GB was a pointed message to AMD's cloud customers. AMD's Infinity Fabric interface will enable high bandwidth, coherent memory communications between Vega 7nm chips and AMD Zen processor chips, such as AMD's Zen2 7nm server chips.