A 40nm 60.64TOPS/W ECC-Capable Compute-in-Memory/Digital 2.25MB/768KB RRAM/SRAM System with Embedded Cortex M3 Microprocessor for Edge Recommendation Systems
Resistive RAM (RRAM) is an exciting technology that exhibits various new properties that have been long absent in traditional charge-based memories. RRAM features high-bit density, non-volatile storage, accurate compute in-memory (CIM), and both process and voltage compatibility. Each of these properties makes RRAM a compelling candidate for Al applications, particularly at the edge. To demonstrate the utility of these properties, we direct our effort to real-world event-driven and memory-constrained applications, such as recommendation systems and natural language processing (NLP). To enable these applications at the edge, higher memory capacity and bandwidth must be achieved despite irregular data access patterns that prevent effective caching and data reuse. Furthermore, we find that these applications are rarely (if ever) run continuously, but instead execution is triggered by events. The combination of these two challenges makes RRAM an ideal candidate given its high density and non-volatility enabling near-zero leakage power and complete power down. To address these challenges, this paper presents a 2.25MB RRAM based CIM accelerator with 765kB of SRAM and an embedded Cortex M3 processor for edge devices.
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A 40nm 60.64TOPS/W ECC-Capable Compute-in-Memory/Digital 2.25MB/768KB RRAM/SRAM System with Embedded Cortex M3 Microprocessor for Edge Recommendation Systems
Semantic Scholar · Engineering · 2022
Abstract
Resistive RAM (RRAM) is an exciting technology that exhibits various new properties that have been long absent in traditional charge-based memories. RRAM features high-bit density, non-volatile storage, accurate compute in-memory (CIM), and both process and voltage compatibility. Each of these properties makes RRAM a compelling candidate for Al applications, particularly at the edge. To demonstrate the utility of these properties, we direct our effort to real-world event-driven and memory-constrained applications, such as recommendation systems and natural language processing (NLP). To enable these applications at the edge, higher memory capacity and bandwidth must be achieved despite irregular data access patterns that prevent effective caching and data reuse. Furthermore, we find that these applications are rarely (if ever) run continuously, but instead execution is triggered by events. The combination of these two challenges makes RRAM an ideal candidate given its high density and non-volatility enabling near-zero leakage power and complete power down. To address these challenges, this paper presents a 2.25MB RRAM based CIM accelerator with 765kB of SRAM and an embedded Cortex M3 processor for edge devices.