Memory remains one of the most critical technologies for enabling continued advances in artificial intelligence/machine learning (AI/ML) processing.
From the rapid development of PCs in the 1990s, to the explosion of gaming in the 2000s, and the emergence of mobile and cloud computing in the 2010s, memory has played an integral role in enabling these new computing paradigms. The memory industry has responded to the needs of the industry over the last 30 years, and is being called upon again to continue innovating as we enter a new age of AI/ML.
PCs drove an increase in memory bandwidth and capacity, as users processed growing amounts of data with applications like Word, Excel, and PowerPoint. Graphical user interfaces, the Web, and gaming pushed performance even higher. This gave rise to a new type of memory called Graphics DDR, designed to meet increased bandwidth demands.
Mobile phones and tablets ushered in a new era with on-the-go computing, and the need for long battery life drove the memory industry to create new Mobile-specific memories to meet the needs of these markets. Today, cloud computing continues to drive increases in capacity and performance to tackle larger workloads from connected devices.
Looking forward, AI/ML applications are driving the need for better memory performance, capacity, and power efficiency, challenging memory system designers on multiple fronts all at the same time. According to OpenAI, AI/ML training capability has increased by a factor of 300,000 between 2012 and 2019—a doubling every 3.43 months. AI/ML models and training sets are growing in size as well, with the largest models now exceeding 170 billion parameters and even larger models on the horizon.
Continue reading: https://www.fierceelectronics.com/electronics/memory-key-to-future-ai-and-ml-performance
From the rapid development of PCs in the 1990s, to the explosion of gaming in the 2000s, and the emergence of mobile and cloud computing in the 2010s, memory has played an integral role in enabling these new computing paradigms. The memory industry has responded to the needs of the industry over the last 30 years, and is being called upon again to continue innovating as we enter a new age of AI/ML.
PCs drove an increase in memory bandwidth and capacity, as users processed growing amounts of data with applications like Word, Excel, and PowerPoint. Graphical user interfaces, the Web, and gaming pushed performance even higher. This gave rise to a new type of memory called Graphics DDR, designed to meet increased bandwidth demands.
Mobile phones and tablets ushered in a new era with on-the-go computing, and the need for long battery life drove the memory industry to create new Mobile-specific memories to meet the needs of these markets. Today, cloud computing continues to drive increases in capacity and performance to tackle larger workloads from connected devices.
Looking forward, AI/ML applications are driving the need for better memory performance, capacity, and power efficiency, challenging memory system designers on multiple fronts all at the same time. According to OpenAI, AI/ML training capability has increased by a factor of 300,000 between 2012 and 2019—a doubling every 3.43 months. AI/ML models and training sets are growing in size as well, with the largest models now exceeding 170 billion parameters and even larger models on the horizon.
Continue reading: https://www.fierceelectronics.com/electronics/memory-key-to-future-ai-and-ml-performance
