Figure 1 Memory at the center of an AI architecture.
Much greater energy efficiencies and speeds can be achieved by storing the entire trained model directly on the AI processor die with low-power, non-volatile memory that is dense and fast. By enabling a new memory-centric architecture, the entire trained model or knowledge base could then be on-chip, connected directly to the neural network, with the potential for massive energy savings and performance improvements, resulting in greatly improved battery life and a better user experience. Today, several next-generation memory technologies are competing to accomplish this.
ReRAM’s potential
The ideal non-volatile embedded memory for AI applications would be very simple to manufacture, easy to integrate in the back-end-of-line of well-understood CMOS processes, easily scaled to advanced nodes, available in high volume, and able to deliver on the energy and speed requirements for these applications.
Resistive RAM (ReRAM) has a much greater ability to scale than magnetic RAM (MRAM) or phase-change memory (PCM) alternatives, an important consideration when looking at 14, 12, and even 7 nm process nodes. These other technologies require more complex and expensive manufacturing processes and more power to operate than ReRAM.
Figure 2 ReRAM can fill a memory-technology gap.
The nanofilament technology of
Crossbar’s ReRAM for instance enables scaling below 10 nm without impacting performance. ReRAM is based on a simple device structure using CMOS-friendly materials and a standard manufacturing process that can be easily integrated with and manufactured on existing CMOS fabs. As it is a low-temperature, back-end-of-line process integration, multiple layers of ReRAM arrays can be integrated on top of CMOS logic wafers to build 3D ReRAM storage.
AI needs the best performance per watt, and this is especially true when applied to power-limited edge devices. ReRAM has demonstrated energy efficiency five times greater than that of DRAM – as many as 1,000 bit reads per nanojoule – while exhibiting better overall read performance than DRAM – up to 12.8 GB/s with less than 20 ns of random latency.
Memory-centric architectures
Scientists are already exploring a variety of novel brain-inspired paradigms to achieve much greater energy efficiencies by imitating the way neurons and synapses of the central nervous system interact. Artificial synapses based on ReRAM technology are a very promising method for enabling these high-density and ultimately scaled synaptic arrays in neuromorphic architectures. ReRAM has the potential to play a significant role in both current and radically new approaches to AI by enabling AI at the edge.
