Compiling ANTs with GNU-6.5

What is Advanced Normalization Tools?

ANTS is a tool for computational neuroanatomy based on medical images. ANTS reads any image type that can be read by ITK (, that is, jpg, tiff, hdr, nii, nii.gz, mha/d and more image types as well. For the most part, ANTS will output float images which you can convert to other types with the ANTS
ConvertImagePixelType tool. ImageMath has a bunch of basic utilities such as multiplication, inversion and many more advanced tools such as computation of the Lipschitz norm of a deformation field. ANTS
programs may be called from the command line on almost any platform.

ANTs project site can be found at GitHub – ANTsX/ANTs: Advanced Normalization Tools (ANTs). Compilation Information can found at Compiling ANTs on Linux and Mac OS · ANTsX/ANTs Wiki · GitHub


  • gnu-6.5
  • m4-1.4.18
  • gmp-6.1.0
  • mpfr-3.1.4
  • mpc-1.0.3
  • isl-0.18
  • gsl-2.1
  • cmake-3.21.3

ANTs can be not too difficult if you use their installation script found here

% mkdir /usr/local/ANTs
% cd /usr/local/ANTs
% git clone
% ./

Once done, you should see in the ANTs directory

ANTs  build  install

Inside ANTs, you can see the install directory where the bin and lib lies

Intel unveil Second-Generation Neuromorphic Chip

Various processors and pieces of code are often compared to brains, but neuromorphic chips work to much more directly mimic neurological systems through the use of computational “neurons” that communicate with one another. Intel’s first-generation Loihi chip, introduced in 2017, has around 128,000 of those digital neurons. Over the ensuing four years, Loihi has been packed into increasingly large systemslearned to touch and even been taught to smell.

Now, it’s getting a new family member: Loihi 2. In its press release, Intel said that years of testing with the first-generation Loihi chip helped them to design a second generation with up to ten times the processing speed; up to 15 times greater resource density; and up to a million computational neurons per chip – more than seven times those in the first generation. Intel reports that early tests have shown that Loihi 2 required more than 60 times fewer ops per inference when running deep neural networks as compared to Loihi 1 (without a loss in accuracy).

Intel Unveils Loihi 2, Its Second-Generation Neuromorphic Chip, HPCWire