[HN Gopher] Dive into deep learning compilers
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       Dive into deep learning compilers
        
       Author : sebg
       Score  : 104 points
       Date   : 2022-02-24 15:44 UTC (7 hours ago)
        
 (HTM) web link (tvm.d2l.ai)
 (TXT) w3m dump (tvm.d2l.ai)
        
       | forgotmyoldacc wrote:
       | Hasn't been updated since 2020:
       | https://github.com/d2l-ai/d2l-tvm, may be severely outdated,
       | since deep learning compilers is a rapidly moving field.
        
       | 37ef_ced3 wrote:
       | Related: https://NN-512.com is a compiler that generates C99 code
       | for neural net inference. It takes as input a simple text
       | description of a convolutional neural net inference graph. It
       | produces as output a human-readable, stand-alone (no
       | dependencies) C99 implementation of that graph. The generated C99
       | code uses AVX-512 vector instructions to perform inference.
        
       | dr_zoidberg wrote:
       | For those surprised at the dot product benchmark on 3.1 [0] where
       | np.dot() is substantially faster when using floats over ints, the
       | things is simply that numpy can use BLAS libraries for floats,
       | but not for ints (because BLAS libs don't implement integer data
       | types).
       | 
       | [0] https://tvm.d2l.ai/chapter_cpu_schedules/arch.html
       | 
       | [1] https://stackoverflow.com/questions/11856293/numpy-dot-
       | produ...
        
       | srvmshr wrote:
       | Dive into DL (d2l) is becoming an incredibly good one stop
       | reference for theory, code and now machine / architecture
       | internals.
        
       | adamnemecek wrote:
       | Seems like automatic differentiation should be covered.
        
       | aghilmort wrote:
       | cool! is this your work?
        
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       (page generated 2022-02-24 23:01 UTC)