tag: memory

The Implications Of AI Everywhere: From Data Center To Edge
By Emma-Jane Crozier - 14 Mar, 2024 - Comments: 0

Generative AI has upped the ante on the transformative force of AI, driving profound implications across all aspects of our everyday lives. Over the past year, we have seen AI capabilities placed firmly in the hands of consumers. The recent news and product announcements emerging from MWC 2024 highlighted what we can expect to see from the next wave of generative AI applications. AI will be eve... » read more

Flash Memory Demystified: NOR Flash Vs. NAND Flash
By Dharini Subashchandran - 14 Mar, 2024 - Comments: 0

By Dharini SubashChandran and Shyam Sharma In the world of flash memory, two primary types dominate the market: NOR flash and NAND flash. While they both serve as essential components in various electronic devices, they differ significantly in terms of structure, functionality, and use cases. In this blog post, we will explore the fundamental differences between NOR flash and NAND flash. ... » read more

The Rising Price Of Power In Chips
By Ed Sperling - 14 Mar, 2024 - Comments: 0

Power is everything when it comes to processing and storing data, and much of it isn't good. Power-related issues, particularly heat, dominate chip and system designs today, and those issues are widening and multiplying. Transistor density has reached a point where these tiny digital switches are generating more heat than can be removed through traditional means. That may sound manageable e... » read more

Securing DRAM Against Evolving Rowhammer Threats
By Aharon Etengoff - 07 Mar, 2024 - Comments: 0

Advanced process nodes and higher silicon densities are heightening DRAM's susceptibility to Rowhammer attacks, as reduced cell spacing significantly decreases the hammer count needed for bit flips. Rowhammer exploits DRAM’s single-capacitor-per-bit design to trigger bit flips in adjacent cells through repeated memory row accesses. This vulnerability allows attackers to manipulate data, re... » read more

HBM3E And GDDR6: Memory Solutions For AI
By Rambus - 06 Mar, 2024 - Comments: 0

AI/ML changes everything, impacting every industry and touching the lives of everyone. With AI training sets growing at a pace of 10X per year, memory bandwidth is a critical area of focus as we move into the next era of computing and enable this continued growth. AI training and inference have unique feature requirements that can be served by tailored memory solutions. Learn how HBM3E and G... » read more

Thanks For The Memories!
By Steve Roddy - 15 Feb, 2024 - Comments: 0

“I want to maximize the MAC count in my AI/ML accelerator block because the TOPs rating is what sells, but I need to cut back on memory to save cost,” said no successful chip designer, ever. Emphasis on “successful” in the above quote. It’s not a purely hypothetical quotation. We’ve heard it many times. Chip architects — or their marketing teams — try to squeeze as much brag-... » read more

Memory’s Future Hinges On Reliability
By Karen Heyman - 14 Feb, 2024 - Comments: 0

Experts at the Table: Semiconductor Engineering sat down to talk about the impact of power and heat on off-chip memory, and what can be done to optimize performance, with Frank Ferro, group director, product management at Cadence; Steven Woo, fellow and distinguished inventor at Rambus; Jongsin Yun, memory technologist at Siemens EDA; Randy White, memory solutions program manager at Keysight; a... » read more

Research Bits: Feb. 13
By Jesse Allen - 13 Feb, 2024 - Comments: 0

Fast phase-change memory Researchers from Stanford University, TSMC, National Institute of Standards and Technology (NIST), and University of Maryland developed a new phase-change memory for future AI and data-centric systems. It is based on GST467, an alloy of four parts germanium, six parts antimony, and seven parts tellurium, which is sandwiched between several other nanometer-thin material... » read more

Research Bits: Jan. 23
By Jesse Allen - 23 Jan, 2024 - Comments: 0

Memristor-based Bayesian neural network Researchers from CEA-Leti, CEA-List, and CNRS built a complete memristor-based Bayesian neural network implementation for classifying types of arrhythmia recordings with precise aleatoric and epistemic uncertainty. While Bayesian neural networks are useful for at sensory processing applications based on a small amount of noisy input data because they ... » read more

Developing ReRAM As Next Generation On-Chip Memory For Machine Learning, Image Processing And Other Advanced CPU Applications
By Brett Lowe - 18 Jan, 2024 - Comments: 0

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, designers are adding additional on-chip memory to their CPUs. Traditionally, SRAM has been the most widely used on-chip CPU memory type. Unfortunately, SRAM is currently limited to a size of hundreds of ... » read more

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