Cache-management scheme that they say improves the data rate of in-package DRAM
Source: Researchers from MIT, Intel, and ETH Zurich
Xiangyao Yu (MIT), Christopher J. Hughes (Intel), Nadathur Satish (Intel) Onur Mutlu (ETH Zurich), Srinivas Devadas (MIT)
As the transistor counts in processors have gone up, the relatively slow connection between the processor and main memory has become the chief impediment to improving computing performance but now, researchers from MIT, Intel, and ETH Zurich have created a cache-management scheme that they say improves the data rate of in-package DRAM caches by 33 to 50 percent.
Foundry forms ASIC subsidiary as it focuses on 14nm/12nm and above.
Growing volume of data and limited improvements in performance create new opportunities for approaches that never got off the ground.
Why auto tech companies are so concerned about interactions with humans.
Company’s push into deep learning opens door to a variety of new architectures, including tiles, advanced packaging and more customized solutions.
Old solutions don’t necessarily work anymore, particularly at advanced nodes and ultra-low voltage.
Costs of developing a complex chip could run as high as $1.5B, while power/performance benefits are likely to decrease.
AI-enabled systems are being designed to process more data locally as device scaling benefits decline.
Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.
Foundry forms ASIC subsidiary as it focuses on 14nm/12nm and above.
Technology has the potential to reshape processing everywhere, starting with limited scientific and commercial applications.
Market overcrowding, more efficient manufacturing, and growing list of scaling issues create a challenging competitive landscape.
Using the Von Neumann architecture for artificial intelligence applications is inefficient. What will replace it?
Experts at the Table, part 1: Why machine learning works in some cases and not in others.
Sticking a bigger/better cache on a CPU doesn’t fix the fundamental problems with the architecture. Really you want to do processor-in-memory (PiM). Most of the NVMe cards for the storage have ARM (or similar) CPUs managing the storage, and it’s a lot quicker to ask them to do the work than shoveling the data back and forth to a central X86 CPU.
This gets worse as you start die-stacking memory for density, but don’t get any more I/O pins (as with pins on a PCIe card), so you need to move the memory controller and compute into the die stacks.
Surfaces and edges are a dimension down from volumes and areas.
I have checked Apple iPhone Support and read something about the way of making the high capacity data caches. It is very important and the efficient as well for the user. We must know about this in detail.