GDDR Accelerates Artificial Intelligence And Machine Learning


The origins of modern graphics double data rate (GDDR) memory can be traced back to GDDR3 SDRAM. Designed by ATI Technologies, GDDR3 made its first appearance in NVidia’s GeForce FX 5700 Ultra card which debuted in 2004. Offering reduced latency and high bandwidth for GPUs, GDDR3 was followed by GDDR4, GDDR5, GDDR5X and the latest generation of GDDR memory, GDDR6. GDDR6 SGRAM supports a ma... » read more

HBM2e Offers Solid Path For AI Accelerators


Today, AI processors are so blazingly fast that they’re constantly having to wait for data from memory. Unfortunately, with the status quo, memory is just not fast enough to unleash the true performance of those new and highly advancing AI processors. In simple terms, AI processor performance is rapidly growing, and memory is not keeping up. This creates a bottleneck, or what Rambus calls the... » read more

Memory Options And Tradeoffs


Steven Woo, Rambus fellow and distinguished inventor, talks with Semiconductor Engineering about different memory options, why some are better than others for certain tasks, and what the tradeoffs are between the different memory types and architectures.     Related Articles/Videos Memory Tradeoffs Intensify In AI, Automotive Applications Why choosing memories and archi... » read more

HBM2 Vs. GDDR6: Tradeoffs In DRAM


Semiconductor Engineering sat down to talk about new DRAM options and considerations with Frank Ferro, senior director of product management at Rambus; Marc Greenberg, group director for product marketing at Cadence; Graham Allen, senior product marketing manager for DDR PHYs at Synopsys; and Tien Shiah, senior manager for memory marketing at Samsung Electronics. What follows are excerpts of th... » read more

Speeding Up AI


Robert Blake, president and CEO of Achronix, sat down with Semiconductor Engineering to talk about AI, which processors work best where, and different approaches to accelerate performance. SE: How is AI affecting the FPGA business, given the constant changes in algorithms and the proliferation of AI almost everywhere? Blake: As we talk to more and more customers deploying new products and... » read more

Engineering The Signal For GDDR6


DDR1 through DDR3 had their challenges, but speeds were below one gigabit and signal integrity (SI) challenges were more centered around static timing and running pseudo random binary sequence (PRBS) simulations. Now, with GDDR6, we are working on 16 to 20 gigabits per second (Gbps) signaling and even faster in the near future. As a result, engineering the signal for GDDR6 will require careful ... » read more

Latency Under Load: HBM2 vs. GDDR6


Steven Woo, Rambus fellow and distinguished inventor, explains why data traffic and bandwidth are critical to choosing the type of DRAM, options for improving traffic flow in different memory types, and how this works with multiple memory types.   Related Video GDDR6 - HBM2 Tradeoffs Why designers choose one memory type over another. Applications for each were clearly delineate... » read more

More Memory And Processor Tradeoffs


Creating a new chip architecture is becoming an increasingly complex series of tradeoffs about memories and processing elements, but the benefits are not always obvious when those tradeoffs are being made. This used to be a fairly straightforward exercise when there was one processor, on-chip SRAM and off-chip DRAM. Fast forward to 7/5nm, where chips are being developed for AI, mobile ph... » read more

GDDR6 And HBM2: Signal Integrity Challenges For AI


In a nutshell, Artificial Intelligence (AI) and its growing list of applications demand a considerably large amount of bandwidth to push bits in and out of memory at the highest speeds possible. AI has been getting a lot of industry attention, and certainly it’s not a new phenomenon because it’s been gaining even greater traction in the last year or two. This is especially true since a n... » read more

GDDR6 – HBM2 Tradeoffs


Steven Woo, Rambus fellow and distinguished inventor, talks about why designers choose one memory type over another. Applications for each were clearly delineated in the past, but the lines are starting to blur. Nevertheless, tradeoffs remain around complexity, cost, performance, and power efficiency.   Related Video Latency Under Load: HBM2 vs. GDDR6 Why data traffic and bandw... » read more

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