Andes Technology and TetraMem Collaborate to Build Groundbreaking AI Accelerator Chip with Analog In-Memory Computing

San Jose, CA - Aug 10, 2023 - Andes Technology, a leading supplier of high-efficiency, low-power 32/64-bit RISC-V processor cores and Founding Premier member of RISC-V International, and TetraMem Inc, a pioneer in analog memristor technology and in-memory computing, are proud to announce a strategic partnership aimed at delivering a fast, highly efficient, AI inference chip that will revolutionize the landscape of artificial intelligence and edge computing.

The convergence of AI and edge computing has become a driving force behind the advancement of numerous industries, including autonomous vehicles, smart cities, healthcare, cybersecurity, and entertainment. Recognizing the immense potential of this market, TetraMem has licensed the powerful Andes RISC-V NX27V vector CPU, combined with ACE (Andes Custom Extension^TM) to create a cutting-edge solution that addresses the challenges of AI processing in power-constrained environments.

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64-bit CPU with RISC-V Vector Extension

The centerpiece of this collaboration is the fusion of Andes' high-performance RISC-V Vector CPU with TetraMem's revolutionary compute memristor - an analog RRAM - in-memory computing architecture through ACE to enable tight coupling for the best performance. This unprecedented combination amplifies the strengths of both companies, resulting in blazingly fast, energy-efficient AI inference that surpasses the limitations of traditional computing approaches - transcending “memory wall” and “end of Moore’s Law” constraints.

Features of the AI Accelerator Chip:

1. RISC-V Vector CPU Excellence: Andes RISC-V Vector CPU cores are known for their exceptional performance, efficiency, and configurability, making them ideal for a wide range of AI and edge computing applications. The addition of Andes powerful vector processor brings unparalleled performance capabilities to the accelerator chip.
2. Analog In-Memory Computing Prowess: TetraMem's unique, analog in-memory computing technology empowers the chip with massively parallel VMM computation without data movement, mitigating the energy overhead of conventional architectures as confirmed in TetraMem’s first commercially manufactured demonstration chip.
3. Energy-Efficient AI Acceleration: The joint effort aims to create a chip that is not only powerful but improves energy-efficient by at least an order of magnitude. By optimizing computations and eliminating transfer of weight data, the planned chip will significantly extend the battery life of edge devices and impose a near-zero impact on thermal budgets.
4. Flexible and Scalable: The AI accelerator chip will be designed from 22nm and beyond, to 7nm and below in the future, with versatility and scalability in mind for easy integration into various AI-powered products and applications. This adaptability ensures broad industry applicability. The TetraMem founding team has demonstrated scalability of the compute memristor to 2nm and below, ensuring a roadmap to future-proof solutions.

Mr. Frankwell Lin, Chairman and CEO of Andes Technology, expressed his enthusiasm for the partnership, saying, "Our collaboration with TetraMem represents a significant milestone in the advancement of AI accelerators. By combining Andes' world-class RISC-V vector processing technology with TetraMem's groundbreaking analog in-memory computing, we are poised to deliver a revolutionary solution that will empower the next generation of AI applications."

Dr. Glenn Ning Ge, CEO of TetraMem Technologies, echoed this sentiment, stating, "TetraMem's analog-RRAM-based in-memory computing technology changes the physics of how AI computations are performed, launching a new era in computing. Working hand in hand with Andes, we are confident that our joint AI accelerator chip will set a new standard for AI processing in terms of speed and energy efficiency."

Tetramem anticipates unveiling the AI accelerator chip and making engineering samples and development kits for the new 22nm “TetraMem MX Series” chip available to the public by the second half of 2024. The partnership between Andes and TetraMem signifies a major leap forward in the field of AI hardware, promising to unlock unprecedented possibilities for AI innovations.

For more information about Andes Technology and TetraMem Technologies, please visit their respective websites at www.andestech.com and www.tetramem.com.

About Andes Technology

Eighteen years in business and a Founding Premier member of RISC-V International, Andes is a publicly-listed company (TWSE: 6533; SIN: US03420C2089; ISIN: US03420C1099) and a leading supplier of high-performance/low-power 32/64-bit embedded processor IP solutions, and the driving force in taking RISC-V mainstream. Its V5 RISC-V CPU families range from tiny 32-bit cores to advanced 64-bit Out-of-Order processors with DSP, FPU, Vector, Linux, superscalar, and/or multi/many-core capabilities. By the end of 2022, the cumulative volume of Andes-Embedded™ SoCs has surpassed 12 billion. For more information, please visit https://www.andestech.com.

About TetraMem Inc

Founded in 2018 by a team of world-class experts, TetraMem is poised to deliver the industry’s most disruptive in-memory computing (IMC) technology for edge applications. The TetraMem team brings together complementary skill sets and technological know-how with 34 patents granted to date spanning materials science, device, circuit design, architecture, and application, as well as a patented six-dimension co-design methodology. TetraMem is the world’s only company to produce a high bit-density multi-level memristor-based accelerator in a commercial foundry, with the technology featured in the March 30, 2023 edition of the journal, Nature. This groundbreaking technology enables memory-based computation, eliminating weight-data movement, substantially improving the energy efficiency and performance of AI and machine learning workloads compared to digital technologies, with scalability well beyond the limits of competing analog technologies. For more information, please visit https://www.tetramem.com. Follow TetraMem on LinkedIn.

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