Why A DSP Is Indispensable In The New World of AI


Chips being designed today for the automotive, mobile handset, AI-IoT (artificial intelligence - Internet of things), and other AI applications will be fabricated in a year or two, designed into end products that will hit the market in three or more years, and then have a product lifecycle of at least five years. These chips will be used in systems with a large number and various types of senso... » read more

IBM’s Energy-Efficient NorthPole AI Unit


At this point it is well known that from an energy efficiency standpoint, the biggest bang for the back is to be found at the highest levels of abstraction. Fitting the right architecture to the task at hand i.e., an application specific architecture, will lead to benefits that are hard or impossible to claw back later in the design and implementation flow.  With the huge increase in the inter... » read more

Your AI Chip Doesn’t Need An Expensive Insurance Policy


Imagine you are an architect designing a new SoC for an application that needs substantial machine learning inferencing horsepower. The team in marketing has given you a list of ML workloads and performance specs that you need to hit. The in-house designed NPU accelerator works well for these known workloads – things like MobileNet v2 and Resnet50. The accelerator speeds up 95+% of the comput... » read more

A Packet-Based Architecture For Edge AI Inference


Despite significant improvements in throughput, edge AI accelerators (Neural Processing Units, or NPUs) are still often underutilized. Inefficient management of weights and activations leads to fewer available cores utilized for multiply-accumulate (MAC) operations. Edge AI applications frequently need to run on small, low-power devices, limiting the area and power allocated for memory and comp... » read more

A Bridge From Mars To Venus


In a now-famous 1992 pop psychology book titled "Men Are from Mars, Women Are from Venus," author John Gray posited that most relationship troubles in couples stem from fundamental differences in socialization patterns between men and women. The analogy that the two partners came from different planets was used to describe how two people could perceive issues in completely different and sometim... » read more

GDDR6 Delivers The Performance For AI/ML Inference


AI/ML is evolving at a lightning pace. Not a week goes by right now without some new and exciting developments in the field, and applications like ChatGPT have brought generative AI capabilities firmly to the forefront of public attention. AI/ML is really two applications: training and inference. Each relies on memory performance, and each has a unique set of requirements that drive the choi... » read more

Issues And Challenges In Super-Resolution Object Detection And Recognition


If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

From Data Center To End Device: AI/ML Inference With GDDR6


Created to support 3D gaming on consoles and PCs, GDDR packs performance that makes it an ideal solution for AI/ML inference. As inference migrates from the heart of the data center to the network edge, and ultimately to a broad range of AI-powered IoT devices, GDDR memory’s combination of high bandwidth, low latency, power efficiency and suitability for high-volume applications will be incre... » read more

Can Compute-In-Memory Bring New Benefits To Artificial Intelligence Inference?


Compute-in-memory (CIM) is not necessarily an Artificial Intelligence (AI) solution; rather, it is a memory management solution. CIM could bring advantages to AI processing by speeding up the multiplication operation at the heart of AI model execution. However, for that to be successful, an AI processing system would need to be explicitly architected to use CIM. The change would entail a shift ... » read more

Nightmare Fuel: The Hazards Of ML Hardware Accelerators


A major design challenge facing numerous silicon design teams in 2023 is building the right amount of machine learning (ML) performance capability into today’s silicon tape out in anticipation of what the state of the art (SOTA) ML inference models will look like in 2026 and beyond when that silicon will be used in devices in volume production. Given the continuing rapid rate of change in mac... » read more

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