Power/Performance Bits: Aug. 24


Low power AI Engineers at the Swiss Center for Electronics and Microtechnology (CSEM) designed an SoC for edge AI applications that can run on solar power or a small battery. The SoC consists of an ASIC chip with RISC-V processor developed at CSEM along with two tightly coupled machine-learning accelerators: one for face detection, for example, and one for classification. The first is a bin... » read more

GDDR6 Memory On The Leading Edge


With the accelerating growth in data traffic, it is unsurprising that the number of hyperscale data centers keeps rocketing skyward. According to analysts at the Synergy Research Group, in nine months (Q2’20 to Q1’21), 84 new hyperscale data centers came online bringing the total worldwide to 625. Hyperscaler capex set a record $150B over the last four quarters eclipsing the $121B spent in ... » read more

Harness System-Level Data To Optimize Many-Core AI And ML Chips


The novel multicore architectures of SoCs for machine learning (ML) and artificial intelligence (AI) applications are expected to deliver huge improvements in power efficiency. However, chip development teams and the customers for their devices face the growing complexity of hardware-software co-optimization, validation, and debug. In short, these SoCs are increasingly difficult to validate and... » read more

Better Optimization For Many-Core AI Chips


The rise of massively parallel computing has led to an explosion of silicon complexity, driven by the need to process data for artificial intelligence (AI) and machine learning (ML) applications. This complexity is seen in designs like the Cerebras Wafer Scale Engine (figure 1), a tiled manycore, multiple wafer die with a transistor count into the trillions and nearly a million compute cores. ... » read more

Changes In Auto Architectures


Automotive architectures are changing from a driver-centric model to one where technology supplements and in some cases replaces the driver. Hans Adlkofer, senior vice president and head of the Automotive Systems Group at Infineon, looks at the different levels of automation in a vehicle, what’s involved in the shift from domain to zonal architectures, why a mix of processors will be required... » read more

Designing Chips In A ‘Lawless’ Industry


The guideposts for designing chips are disappearing or becoming less relevant. While engineers today have many more options for customizing a design, they have little direction about what works best for specific applications or what the return on investment will be for those efforts. For chip architects, this is proving to be an embarrassment of riches. However, that design freedom comes wit... » read more

Challenges For New AI Processor Architectures


Investment money is flooding into the development of new AI processors for the data center, but the problems here are unique, the results are unpredictable, and the competition has deep pockets and very sticky products. The biggest issue may be insufficient data about the end market. When designing a new AI processor, every design team has to answer one fundamental question — how much flex... » read more

Sweeping Changes Ahead For Systems Design


Data centers are undergoing a fundamental change, shifting from standard processing models to more data-centric approaches based upon customized hardware, less movement of data, and more pooling of resources. Driven by a flood of web searches, Bitcoin mining, video streaming, data centers are in a race to provide the most efficient and fastest processing possible. But because there are so ma... » read more

Sensor Fusion Everywhere


How do you distinguish between background noise and the sound of an intruder breaking glass? David Jones, head of marketing and business development for intuitive sensing solutions at Infineon, looks at what types of sensors are being developed, what happens when different sensors are combined, what those sensors are being used for today, and what they will be used for in the future. » read more

Safe And Robust Machine Learning


Deploying machine learning in the real world is a lot different than developing and testing it in a lab. Quenton Hall, AI systems architect at Xilinx, examines security implications on both the inferencing and training side, the potential for disruptions to accuracy, and how accessible these models and algorithms will be when they are used at the edge and in the cloud. This involves everything ... » read more

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