Thanks For The Memories!


“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

AI Tradeoffs At The Edge


AI is impacting almost every application area imaginable, but increasingly it is moving from the data center to the edge, where larger amounts of data need to be processed much more quickly than in the past. This has set off a scramble for massive improvements in performance much closer to the source of data, but with a familiar set of caveats — it must use very little power, be affordable... » read more

Dealing With Noise In Image Sensors


The expanding use and importance of image sensors in safety-critical applications such as automotive and medical devices has transformed noise from an annoyance into a life-threatening problem that requires a real-time solution. In consumer cameras, noise typically results in grainy images, often associated with poor lighting, the speed at which an image is captured, or a faulty sensor. Typi... » read more

Chip Industry Week In Review


By Jesse Allen, Karen Heyman, and Liz Allan Renesas will acquire Transphorm, which designs and manufactures gallium nitride power devices, for about $339 million. GaN, which is a wide-bandgap technology, is used for high-voltage applications in a slew of markets, including EVs and EV fast chargers, as well as data centers and industrial applications. Cadence acquired Invecas, a provider o... » read more

Chip Industry Silos Are Crimping Advances


Change is never easy, but it is more difficult when it involves organizational restructuring. The pace of such restructuring has been increasing over the past decade, and often it is more difficult to incorporate than technological advancements. This is due to the siloed nature of the semiconductor industry, both within the industry itself, and its relationship to surrounding industries. Inc... » read more

Is Transformer Fever Fading?


The hottest, buzziest thing bursts onto the scene and captures the attention of the business press and even the general public. Scads of articles and videos are published about The Hot Thing. And then, in the blink of an eye, the world’s attention shifts to the Next New Thing! Are we talking about the latest pop song that leads the Spotify streaming charts? Perhaps a new fashion trend that... » read more

SRAM’s Role In Emerging Memories


Experts at the Table — Part 3: Semiconductor Engineering sat down to talk about AI, the latest issues in SRAM, and the potential impact of new types of memory, with Tony Chan Carusone, CTO at Alphawave Semi; Steve Roddy, chief marketing officer at Quadric; and Jongsin Yun, memory technologist at Siemens EDA. What follows are excerpts of that conversation. Part one of this conversation can be ... » read more

Dramatic Changes Ahead For Chips And Systems


Early this year, most people had never heard of generative AI. Now the entire world is racing to capitalize on it, and that's just the beginning. New markets, such as spatial computing, quantum computing, 6G, smart infrastructure, sustainability, and many more are accelerating the need to process more data faster, more efficiently, and with much more domain specificity. Compared to the days ... » read more

BYO NPU Benchmarks


In our last blog post, we highlighted the ways that NPU vendors can shade the truth about performance on benchmark networks such that comparing common performance scores such as “Resnet50 Inferences / Second” can be a futile exercise. But there is a straight-forward, low-investment method for an IP evaluator to short-circuit all the vendor shenanigans and get a solid apples-to-apples result... » read more

Data Formats For Inference On The Edge


AI/ML training traditionally has been performed using floating point data formats, primarily because that is what was available. But this usually isn't a viable option for inference on the edge, where more compact data formats are needed to reduce area and power. Compact data formats use less space, which is important in edge devices, but the bigger concern is the power needed to move around... » read more

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