New Efficiency Benchmark: Faster And Extremely Accurate Quantized Pc Energy Measurement
New Efficiency Benchmark: Faster And Extremely Accurate Quantized Pc Energy Measurement ::: https://tlniurl.com/2tkR8J
a, Cross-layer co-optimizations across the full stack of the design enable NeuRRAM to simultaneously deliver high versatility, computational efficiency and software-comparable inference accuracy. b, Micrograph of the NeuRRAM chip. c, Reconfigurability in various aspects of the design enables NeuRRAM to implement diverse AI models for a wide variety of applications. d, Comparison of EDP, a commonly used energy-efficiency and performance metric among recent RRAM-based CIM hardware. e, Fully hardware-measured inference accuracy on NeuRRAM is comparable to software models quantized to 4-bit weights across various AI benchmarks.
Table 3 presents the different run-time delays for our FPGA implementation as compared to different GPU implementations. In all cases, the average run-time delay per image ΔTimage is obtained by averaging the total computation time over a large number of input images. It is notable that the FPGA outweighs the GPU in sense of speed for this application in case of a single input image (i.e., Batch size = 1), while the GPU shows less run-time delay for larger batch sizes. An improvement of 4.2 times has been achieved by moving from GPU implementation, in which the average run-time delay per image is 7.01 ms, to the FPGA implementation, in which the average run-time delay per image is decreased to 1.669 ms. For batch sizes greater than 1, the GPU shows smaller run-time delays (hence are faster) as compared with our single input FPGA implementation. However, we will comment on this fact later in the energy efficiency analysis part of the results.
Because quantized neural networks rely on simple operations, they are a prime target for exploiting the use of PiM to process AI kernels at the edge. Performing more straightforward computation close to where data resides greatly reduces the overall movement of data, which improves latency, throughput, and energy efficiency. These can likely be applied even more efficiently to quantized and binary CNNs, which use XNOR and bit count operators. 59ce067264
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