Cerebras’ new 2.6 trillion transistors wafer scale chip is one the announcements made during the 2021 edition of the Linley Spring Processor Conference, a virtual event organized by technology analysis firm The Linley Group from April 19 to 23. In our quick overview of the conference we will focus mainly on new product announcements, which include innovative AI intellectual property from startups Expedera and EdgeCortix, a new approach to clock distribution from Movellus, and more. But first, let’s briefly summarize the opening keynote given by Linley Gwennap – Principal Analyst of The Linley Group – who provided an updated overview of AI technology and market trends.
A variety of AI acceleration architectures
Gwennap described the different AI processing architectures that the industry has developed over the past few years. While many CPUs, GPUs, and DSPs include wide vector (SIMD) compute units, many AI accelerators use systolic arrays to break the register-file bottleneck. Also, convolution architecture optimized for CNNs have been proposed: examples include processors developed by Alibaba and Kneron. Within AI-specialized architectures, many choices are possible: a processor can use many little cores, or a few big cores. Extreme examples are Cerebras with its wafer-scale chip integrating over 400,000 cores (850,000 in the latest version), and Groq with one mega-core only. Little cores are easier to design, while big cores simplify compiler/software design and are better for real-time workloads. Another architectural choice is between multicore versus dataflow: in a multicore design, each core executes the neural network from start to finish, while in a dataflow design the neural network is divided across many cores. An additional architectural style – that goes ‘beyond cores’ – is Coarse-Grain Reconfigurable Architecture (CGRA), which uses dataflow principles, but instead of cores, the connected blocks contain pipelined compute and memory units. This approach has been adopted by SambaNova, SimpleMachines, Tsing Micro and others. So the industry now offers a wide range of AI-capable architectures, ranging from very generic to very specialized. In general terms, a higher degree of specialization translates into higher efficiency but lower flexibility.
