The Next Phase Of Computing


Apple's new M1 chip offers a glimpse of what's ahead, and not just from Apple. Being able to get 18 to 20 hours of battery life from a laptop computer moves the ball much farther down the field in semiconductor design. All of this is entirely dependent on the applications, of course. But what's important here is how much battery life and performance can be gained by designing hardware specif... » read more

Designs Beyond The Reticle Limit


Designs continue to grow in size and complexity, but today they are reaching both physical and economic challenges. These challenges are causing a reversal of the integration trend that has provided much of the performance and power gains over the past couple of decades. The industry, far from giving up, is exploring new ways to enable designs to go beyond the reticle size, which is around 8... » read more

Week In Review: Design, Low Power


Tools & IP Cadence debuted System-Level Verification IP (System VIP), a suite of tools and libraries for automating SoC testbench assembly, bus and CPU traffic generation, cache-coherency validation, and system performance bottleneck analysis. Tests created using the System VIP solution are portable across Cadence simulation, emulation and prototyping engines and can also be extended to po... » read more

Sensor Fusion Challenges In Cars


The automotive industry is zeroing in on sensor fusion as the best option for dealing with the complexity and reliability needed for increasingly autonomous vehicles, setting the stage for yet another shift in how data from multiple devices is managed and utilized inside a vehicle. The move toward greater autonomy has proved significantly more complicated than anyone expected at first. There... » read more

System-Level Packaging Tradeoffs


Leading-edge applications such as artificial intelligence, machine learning, automotive, and 5G, all require high bandwidth, higher performance, lower power and lower latency. They also need to do this for the same or less money. The solution may be disaggregating the SoC onto multiple die in a package, bringing memory closer to processing elements and delivering faster turnaround time. But ... » read more

Deals That Change The Chip Industry


Nvidia's pending $40 billion acquisition of Arm is expected to have a big impact on the chip world, but it will take years before the effects of this deal are fully understood. More such deals are expected over the next couple of years due to several factors — there is a fresh supply of startups with innovative technology, interest rates are low, and market caps and stock prices of buyers ... » read more

Have Processor Counts Stalled?


Survey data suggests that additional microprocessor cores are not being added into SoCs, but you have to dig into the numbers to find out what is really going on. The reasons are complicated. They include everything from software programming models to market shifts and new use cases. So while the survey numbers appear to be flat, market and technology dynamics could have a big impact in resh... » read more

Nvidia To Buy Arm For $40B


Nvidia inked a deal with Softbank to buy Arm for $40 billion, combining the No. 1 AI/ML GPU maker with the No. 1 processor IP company. Assuming the deal wins regulatory approval, the combination of these two companies will create a powerhouse in the AI/ML world. Nvidia's GPUs are the go-to platform for training algorithms, while Arm has a broad portfolio of AI/ML processor cores. Arm also ha... » read more

New Architectures, Much Faster Chips


The chip industry is making progress in multiple physical dimensions and with multiple architectural approaches, setting the stage for huge performance increases based on more modular and heterogeneous designs, new advanced packaging options, and continued scaling of digital logic for at least a couple more process nodes. A number of these changes have been discussed in recent conferences. I... » read more

Assessing Synchronization And Graphics-Compute-Graphics Hazards


In modern rendering environments, there are a lot of cases where a compute workload is used during a frame. Compute is generic (non-fixed function) parallel programming on the GPU, commonly used for techniques that are either challenging, outright impossible, or simply inefficient to implement with the standard graphics pipeline (vertex/geometry/tessellation/raster/fragment). In gener... » read more

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