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AI processor maker BrainChip, which makes ultra-low-power neuromorphic chips and supporting software, and SiFive, founder of the RISC-V computing genre, today announced they have combined their respective technologies to offer chip designers optimized artificial intelligence (AI) and machine learning (ML) for edge computing
BrainChip’s AI engine, Akida, is an advanced neural networking processor architecture that brings AI functionality to edge and cloud computing in a way that wasn’t previously possible with its high performance and ultra-low power usage, the company said. SiFive Intelligence solutions, with their highly configurable multi-core, multi-cluster capable design, integrate software and hardware to accelerate AI/ML applications, BrainChip CMO Jerome Nadel told VentureBeat.
The integration of BrainChip’s Akida technology and SiFive’s multi-core capable RISC-V processors is expected to provide an efficient solution for integrated edge AI computing, Nadel said.
RISC-V (pronounced “risk-five”) is an open instruction-set computing architecture based on established reduced instruction set computing (RISC) principles. It’s an open-source project available to anybody who wants to use it. RISC-V represents a major step forward in data processing — speed that is required of all the new and much “heavier” applications (such as machine learning, AI and high-resolution video) that are coming into daily use. RISC-V appears to be a natural fit for BrainChip’s architecture for neural networking processors.
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AI engine mimics the human brain
SiFive Intelligence-based processors have a highly configurable multi-core, multi-cluster-capable design that has been optimized for a range of applications requiring high-throughput, single-thread performance while under tight power and area constraints, Nadel said.
BrainChip’s Akida mimics the human brain to analyze only essential sensor inputs at the point of acquisition, processing data with efficiency, precision, and economy of energy, Nadel said. Keeping AI/ML local to the chip and independent of the cloud reduces latency while improving privacy and data security, he said.
BrainChip’s technology is based on its spiking neuron adaptive processor (SNAP) technology and licenses SNAP with technology partners. SNAP offers a development solution for companies entering the neuromorphic semiconductor chip market. It is a core-enabling technology in neuromorphic semiconductor chips that enables various applications, such as gaming, cybersecurity, robotic technology and stock market forecasting, among others.
“As we expand our ecosystem of portfolio partners, we want to be sure that our relationships are built on complementary technologies, enabling capabilities and breadth of environments so that we can expand opportunities to as many potential customers as possible,” Nadel said. “Driving our technology into a SiFive-based subsystem is exactly the type of partnership that meets these goals.”
3 questions for BrainChip
VentureBeat asked Jack Kang, senior vice president of Business Development, Customer Experience (CX), Corporate Marketing at SiFive, a few specific questions about the news and the relevance of the partnership.
VentureBeat: What is the no. 1 business takeaway from this announcement?
Jack Kang: For SiFive, this announcement shows the ongoing uptake of the SiFive Intelligence family of RISC-V-based processor IP. More companies are choosing RISC-V to be part of their product roadmap strategy, and SiFive is the leading provider of commercial RISC-V IP. In the emerging greenfield markets of AI/ML-enabled platforms, such as the edge processing market targeted by BrainChip, the performance per area and efficiency advantages of SiFive processor architecture make the SiFive Intelligence family a competitive choice.
VentureBeat: Does BrainChip use any of Arm’s IP in its chips? Arm is known for low power and high performance.
Kang: BrainChip has discussed Arm IP for their product line. Arm processors have built a reputation for low power based on comparisons to x86-based products. SiFive Intelligence products compare well to Arm products through offering improved performance-per-area of up to 30%, combined with a single ISA for simpler programming, and a modular approach that aligns well to working with hardened AI IP such as that developed by BrainChip.
VentureBeat: Can you expand upon this statement: “(Brainchip) mimics the human brain to analyze only essential sensor inputs at the point of acquisition.”
Kang: This statement refers to the ability of humans to focus on what’s important. For example, listening to a conversation in a coffee shop while still registering and acknowledging background sounds. The BrainChip solution will mimic this ability to reduce power and increase efficiency by focusing on the important data being processed. This is similar to, but a step beyond, the adoption of mixed and lower precision data types (INT8 vs. FP16) to speed up and improve the efficiency of AI/ML processing.
Competitors in the market
Brainchip, based in Aliso Viejo, California, competes in the burgeoning intelligent-edge chip market with Nvidia Deep Learning GPU, Keras, TFLearn, Clarifai and Microsoft Cognitive Toolkit, AWS Deep Learning AMIs and Torch. Nvidia owns about 80 percent of the global GPU (graphics processing unit) market. G2.com has market information here. Availability of the new SiFive/BrainChip solutions will be announced at a later date.
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