Executive Folder
Every arithmetic core in every chip on Earth from the smallest IoT sensor to the largest AI cluster burns the vast majority of its energy not on computing, but on moving data. The Adder is the fundamental unit of all computation, but after decades of redesigning it still relies on carry-propagation, and it still wastes 95% of its energy moving data between memory arrays, and arithmetic units located in a distant region of the processor. The Adder itself is a masterpiece of optimization that hasn't changed fundamentally since the 1960s (carry-lookahead, prefix trees, parallel prefix, etc.). All variants of the same algorithm, all hitting the same physical walls.
We didn't try to make the old algorithm faster or better. We replaced it.
Array's patented Simple and Linear Fast Adder routes as a perfectly rectangular array that colocates arithmetic logic and memory cells next to each other at the bit level, achieving a Compute-In-Memory architecture without exotic materials or analog noise — just standard edge-triggered flip-flops and CMOS logic. We exist to position this architecture as the foundation for a new era of computing powering AI, cryptography, edge devices, Bitcoin mining, and every workload limited by arithmetic throughput and memory bandwidth. The array is based on a novel definition of multiple-input addition that side-steps the carry-over paradigm entirely. Being an adder with a simple hardware-based instruction set, the block is primitive enough to drop into existing flows without disturbing the surrounding stack. Its scalable array structure maps naturally into Hash Cores, and Multiply-Accumulate Circuits (common arithmetic block crucial for Digital Signal Processing, AI and Machine Learning). Our long-term vision is for this architecture to become the benchmark for efficient computation across industries.
References for Market Analysis
Coin Desk
Crypto Compare
Executive Briefing on Emerging Technology: Neuromorphic Computing
Organizations are seeking significant performance and sustainability improvements for AI applications. The attached presentation shows digital leaders how neuromorphic computing can deliver business advantages such as significantly lower power implementations, low latency and high scalability.
Hype Cycle for In-Memory Computing Technology, 2017
The digitalization of business generates an inexhaustible demand for faster performance, greater scalability and deeper real-time insight, which is boosting innovation around IMC technologies. Here, we evaluate the maturity and industry adoption of IMC technologies and IMC-enabled solutions.
Market Guide for In-Memory Computing Technologies
Application leaders must identify providers and technologies required for a successful in-memory computing strategy. Digital business speed, scalability, real-time insight and architectural simplification needs are surfacing IMC strategic relevance, while driving its growing adoption.
AI Chip Tectonics Should Give Asian Vendors the Edge
Gartner sees three regional powerhouses eventually dominating the AI chip market. The U.S. is enjoying the early leadership position. Taiwan is gaining share through technological leadership, cutting-edge design and manufacturing, and its role as one of the world’s premier enablers of custom AI silicon. China is gaining share by sheer volume, capturing its enormous domestic market through a state-mandated push for self-sufficiency.
AI Maturity Matters: Proportion of AI and GenAI Prototypes Making It Into Production
The 2024 Gartner AI Mandates for the Enterprise Survey reveals that organizations find it challenging to produce AI prototypes that successfully transition into production. On average, only 41% of generative AI prototypes and 42% of nongenerative AI prototypes reached production. CIOs and AI leaders can use this interactive tool to further discover data insights.
McKinsey & Company. McKinsey Technology Trends Outlook 2025
AI is also the primary catalyst for another trend we highlight this year: application-specific semiconductors. While Moore’s Law and the semiconductor layer of the technology stack have long been key enablers of other tech trends, innovations in semiconductors have spiked as reflected in quantitative metrics such as number of patents. These innovations have come in response to exponentially higher demands for computing capacity, memory, and networking for AI training and inference, as well as a need to manage cost, heat, and electric power consumption. This has given rise to a slew of new products, new competitors, and new ecosystems.
Markets And Markets
Semiconductor Intellectual Property Market Forecast Report - Global Forecast to 2029
Non-Volatile Memory Market - Global Forecsat to 2027
Neuromorphic Computing Market Size, Share & Growth - Global Forecast to 2030
Data Center Chip Market Size, Share and Trends - Global Forecast to 2030
Precedence Research
Generative AI Chipset Market Size, Share and Trends 2025 to 2034
Generative AI Chipset Market (By Chipset Type: CPU, GPU, FPGA, ASIC, Others; By Application: Machine Learning, Deep Learning, Reinforcement Learning, Generative Adversarial Networks (Gans), Natural Language Understanding (NLU): By End-use; Consumer Electronics, Automotive, Healthcare, Retail, Manufacturing, Banking, Financial Services, And Insurance (BFSI), Telecommunication, Others (Energy, Government, Etc.)) - Global Industry Analysis, Size, Trends, Leading Companies, Regional Outlook, and Forecast 2025 to 2034
In-Memory Computing Market Powers Real-Time Intelligence and Speed
The global in-memory computing market size is calculated at USD 24.50 billion in 2025 and is expected to be worth around USD 97.06 billion by 2034. The market is slated to expand at 16.53% CAGR from 2025 to 2034. The in‑memory computing market is gaining rapid traction as organizations embrace real‑time data access and analytics to drive smarter operations.
3D-Stacked Processor Market Size, Share and Trends 2025 to 2034
Global Industry Analysis, Size, Trends, Leading Companies, Regional Outlook, and Forecast 2025-2034.