An Eight-Core RISC-V Processor With Compute Near Last Level Cache in Intel 4 CMOS

被引:9
|
作者
Chen, Gregory K. K. [1 ]
Knag, Phil C. C. [1 ]
Tokunaga, Carlos [1 ]
Krishnamurthy, Ram K. K. [1 ]
机构
[1] Intel Corp, Circuit Res Lab, Hillsboro, OR 97124 USA
关键词
Cache memory; deep learning process-ing; machine learning processing; near-memory computation; RISC-V; single instruction multiple data (SIMD); PERFORMANCE; ENERGY; COST;
D O I
10.1109/JSSC.2022.3228765
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
An eight-core 64-b processor extends RISC-V to perform multiply-accumulate (MAC) within the shared last level cache (LLC). Instead of moving data from the LLC to the core, compute near last level cache (CNC) adds MAC to the LLC datapath and performs computation near where the data are stored. The RV64GC CNC instruction set architecture (ISA) extension performs digital MAC near unmodified SRAM arrays and has a low area overhead of 1.4%. CNC increases memory access width to 512 b per instruction by avoiding bottlenecks in the on-chip networks. The operation also reduces data movement by keeping MAC results and most input operands local to the LLC slices. CNC supports computation on cached data from main memory, coherent data sharing between cores, and virtual addressing. The CNC instructions are included in C++ programs and run either baremetal or in Linux. The 1.15-GHz chip reduces energy consumption by 52x for fully connected and 29x for convolutional deep neural network (DNN) layers, compared to scalar operation. Two benchmarks are characterized: MLPerf Tiny Anomaly Detection v0.5 latency is reduced by 4.25x to 40 mu s versus previous work, and inference latency on memory-augmented neural networks is improved by 4.1x versus scalar operation.
引用
收藏
页码:1117 / 1128
页数:12
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