Research Bits: August 29


Resistive switching with hafnium oxide Researchers from the University of Cambridge, Purdue University, University College London, Los Alamos National Laboratory, and University at Buffalo used hafnium oxide to build a resistive switching memory device that processes data in a similar way as the synapses in the human brain. At the atomic level, hafnium oxide has no structure, with the hafni... » read more

Research Bits: August 22


Photonic memory Researchers from Zhejiang University, Westlake University, and the Chinese Academy of Sciences developed a 5-bit photonic memory capable of fast volatile modulation and proposed a solution for a nonvolatile photonic network supporting rapid training. This was made possible by integrating the low-loss phase-change material (PCM) antimonite (Sb2S3) into a silicon photonic plat... » read more

Week In Review: Design, Low Power


Synopsys’ board of directors appointed Sassine Ghazi as president and chief executive officer effective on Jan. 1, 2024. Ghazi, who is currently the COO, will succeed Aart de Geus, co-founder, chair, and CEO of Synopsys, who will then become the executive chair of board of directors. IBM Research introduced  an energy-efficient mixed-signal analog AI chip for DNN inferencing and demonstra... » read more

Research Bits: August 15


Using noise for spintronics Researchers from the Institute for Basic Science built a vertical magnetic tunneling junction device by sandwiching a few layers of vanadium in tungsten diselenide (V-WSe2), a magnetic material, between top and bottom graphene electrodes to create high-amplitude Random Telegraph Noise (RTN) signals. Through the resistance measurement experiments using these devic... » read more

Processor Tradeoffs For AI Workloads


AI is forcing fundamental shifts in chips used in data centers and in the tools used to design them, but it also is creating gaps between the speed at which that technology advances and the demands from customers. These shifts started gradually, but they have accelerated and multiplied over the past year with the rollout of ChatGPT and other large language models. There is suddenly much more... » read more

Virtual Development Of Perception Sensor Systems


By Ron Martin and Christoph Sohrmann Over the past few years, there has been a marked expansion in research and development activities related to driver assistance systems as well as highly automated and connected driving systems. However, this has yet to translate into a higher degree of automation in the average production vehicle – especially for SAE Level 3 and above. The next step in ... » read more

Specialization Vs. Generalization In Processors


Academia has been looking at specialization for many years, but solutions were rejected because general-purpose solutions were advancing fast enough to keep up with most application requirements. That is no longer the case. The introduction and support of the RISC-V processor architecture has attracted a lot of attention, but whether that is the right direction for the majority of modern comput... » read more

Using A Retimer To Extend Reach For PCIe 6.0 Designs


One of the biggest changes that came with PCIe 6.0 was the transition from non-return-to-zero (NRZ) signaling to PAM4 signaling. Pulse Amplitude Modulation (PAM) enables more bits to be transmitted at the same time on a serial channel. In PCIe 6.0, this translates to 2 bits per clock cycle for 4 amplitude levels (00, 01, 10, 11) vs. PCIe 5.0, and earlier generations, which used NRZ with 1 bit p... » read more

MRAM Getting More Attention At Smallest Nodes


Magneto-resistive RAM (MRAM) appears to be gaining traction at the most advanced nodes, in part because of recent improvements in the memory itself and in part because new markets require solutions for which MRAM may be uniquely qualified. There are still plenty of skeptics when it comes to MRAM, and lots of potential competitors. That has limited MRAM to a niche role over the past couple de... » read more

Developing Energy-Efficient AI Accelerators For Intelligent Edge Computing And Data Centers


Artificial intelligence (AI) accelerators are deployed in data centers and at the edge to overcome conventional von Neumann bottlenecks by rapidly processing petabytes of information. Even as Moore’s law slows, AI accelerators continue to efficiently enable key applications that many of us increasingly rely on, from ChatGPT and advanced driver assistance systems (ADAS) to smart edge device... » read more

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