tag: Quadric

Fallback Fails Spectacularly
By Steve Roddy - 16 May, 2024 - Comments: 0

Conventional AI/ML inference silicon designs employ a dedicated, hardwired matrix engine – typically called an “NPU” – paired with a legacy programmable processor – either a CPU, or DSP, or GPU. The common theory behind these two-core (or even three core) architectures is that most of the matrix-heavy machine learning workload runs on the dedicated accelerator for maximum efficienc... » read more

Is The Transformer Era Over?
By Quadric.io - 15 May, 2024 - Comments: 0

The idea of transformer networks has existed since the seminal publication of the Attention is All You Need paper by Google researchers in June 2017.  And while transformers quickly gained traction within the ML research community, and in particular demonstrated superlative results in vision applications (ViT paper), transformer networks were definitely not a topic of trendy conversation ar... » read more

Fundamental Issues In Computer Vision Still Unresolved
By Karen Heyman - 02 May, 2024 - Comments: 1

Given computer vision’s place as the cornerstone of an increasing number of applications from ADAS to medical diagnosis and robotics, it is critical that its weak points be mitigated, such as the ability to identify corner cases or if algorithms are trained on shallow datasets. While well-known bloopers are often the result of human decisions, there are also fundamental technical issues that ... » read more

Dealing With AI/ML Uncertainty
By Karen Heyman - 25 Apr, 2024 - Comments: 0

Despite their widespread popularity, large language models (LLMs) have several well-known design issues, the most notorious being hallucinations, in which an LLM tries to pass off its statistics-based concoctions as real-world facts. Hallucinations are examples of a fundamental, underlying issue with LLMs. The inner workings of LLMs, as well as other deep neural nets (DNNs), are only partly kno... » read more

Architecting Chips For High-Performance Computing
By Ann Mutschler - 15 Apr, 2024 - Comments: 0

The world’s leading hyperscaler cloud data center companies — Amazon, Google, Meta, Microsoft, Oracle, and Akamai — are launching heterogeneous, multi-core architectures specifically for the cloud, and the impact is being felt in high-performance CPU development across the chip industry. It's unlikely that any these chips will ever be sold commercially. They are optimized for specific ... » read more

Hybrid Architecture Blends Best Of Both Worlds
By Steve Roddy - 11 Apr, 2024 - Comments: 0

Quadric chose the brand name Chimera to describe the company’s novel general purpose neural processing unit (GPNPU) architecture. According to the online Oxford dictionary, in biology a chimera is “an organism containing a mixture of genetically different tissues (or DNA).” Quadric made that naming choice to reflect the fact that its Chimera GPNPU has characteristics of both conventiona... » read more

Embrace The New!
By Steve Roddy - 14 Mar, 2024 - Comments: 0

The ResNet family of machine learning algorithms was introduced to the AI world in 2015. A slew of variations was rapidly discovered that at the time pushed the accuracy of ResNets close to the 80% threshold (78.57% Top 1 accuracy for ResNet-152 on ImageNet). This state-of-the-art performance at the time, coupled with the rather simple operator structure that was readily amenable to hardware ac... » 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

AI Tradeoffs At The Edge
By Ann Mutschler - 15 Feb, 2024 - Comments: 0

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
By Karen Heyman - 01 Feb, 2024 - Comments: 0

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

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