Startup Funding: May 2020


It was a good month for semiconductor startups, with investment spanning a larger company in later funding rounds to brand new seed funding for two chip manufacturing startups. Two AI hardware startups bridge data center and edge, plus EV companies around the world get funding. In total, the eighteen startups profiled this month raised $446.3 million. Semiconductor & design Shanghai-based ... » read more

The Murky World Of AI Benchmarks


AI startup companies have been emerging at breakneck speed for the past few years, all the while touting TOPS benchmark data. But what does it really mean and does a TOPS number apply across every application? Answer: It depends on a variety of factors. Historically, every class of design has used some kind of standard benchmark for both product development and positioning. For example, SPEC... » read more

Stream Vs. Pool Data Processing


Geoff Tate, CEO of Flex Logix, looks at the very different data processing requirements at the edge and in the data center, and what really drives efficiency and speed in applications such as automotive. » read more

Reliability Challenges Grow For 5/3nm


Ensuring that chips will be reliable at 5nm and 3nm is becoming more difficult due to the introduction of new materials, new transistor structures, and the projected use of these chips in safety- and mission-critical applications. Each of these elements adds its own set of challenges, but they are being compounded by the fact that many of these chips will end up in advanced packages or modul... » read more

PCIe 5.0 Drill-Down


Suresh Andani, senior director of product marketing for SerDes IP at Rambus, digs into the new PCI Express standard, why it’s so important for data centers, how it compares with previous versions of the standard, and how it will fit into existing and non-von Neumann architectures. » read more

MLPerf Benchmarks


Geoff Tate, CEO of Flex Logix, talks about the new MLPerf benchmark, what’s missing from the benchmark, and which ones are relevant to edge inferencing. » read more

Using FPGAs For AI


Artificial intelligence (AI) and machine learning (ML) are progressing at a rate that is outstripping Moore's Law. In fact, they now are evolving faster than silicon can be designed. The industry is looking at all possibilities to provide devices that have the necessary accuracy and performance, as well as a power budget that can be sustained. FPGAs are promising, but they also have some sig... » read more

Multiphysics Simulations for AI Silicon to System Success


Achieving power efficiency, power integrity, signal integrity, thermal integrity and reliability is paramount for enabling product success by overcoming the challenges of size and complexity in AI hardware and optimizing the same for rapidly evolving AI software. ANSYS’ comprehensive chip, package and system solutions empower AI hardware designers by breaking down design margins and siloed de... » read more

Chiplets, Faster Interconnects, More Efficiency


Big chipmakers are turning to architectural improvements such as chiplets, faster throughput both on-chip and off-chip, and concentrating more work per operation or cycle, in order to ramp up processing speeds and efficiency. Taken as a whole, this represents a significant shift in direction for the major chip companies. All of them are wrestling with massive increases in processing demands ... » read more

Bottlenecks For Edge Processors


New processor architectures are being developed that can provide two to three orders of magnitude improvement in performance. The question now is whether the performance in systems will be anything close to the processor benchmarks. Most of these processors doing one thing very well. They handle specific data types and can accelerate the multiply-accumulate functions for algorithms by distri... » read more

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