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Flex Logix Accelerates DSP

April 28, 2020

Author: Mike Demler

Recently, Flex Logix customers recognized the common mathematical foundation shared by CNNs and DSPs, leading the embedded-FPGA developer to evaluate the performance of its NNMax deep-learning accelerators (DLAs) on finite-impulse-response (FIR) filters. Flex developed NNMax as licensable intellectual property (IP), which it offers as a tile comprising systolic multiply-accumulate (MAC) arrays, memory blocks, programmable logic, and the company’s XFLX configurable interconnect.

MAC operations typically consume 90% or more of the cycles in a CNN, but FIR filters employ the same operations. In a CNN, each artificial neuron multiplies its inputs by a set of weights (also called a filter), adds the result, and feeds the sum forward as an input to the next layer. FIR filters are similar, comprising a series of MAC operations called filter taps. Each stage in the filter multiplies the input signal by a filter coefficient, combines the result with the output of the previous filter tap, and sends the sum to the next tap.

Flex Logix plans to develop a DSP compiler that maps Matlab output to NNMax. The DLA’s ability to serve as both a DLA and a DSP accelerator will be attractive to some customers, and the initial performance looks promising. But for NNMax to be useful beyond FIR filters, the company must develop a more comprehensive function library, which will likely require new architectural features as well.      

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