Distributed Arithmetic strategy for Dense, Conv1/2D, and EinsumDense

[calad0i]

Description

This PR introduces a new strategy, distributed_arithmetic for

• Dense (io parallel / stream)
• Conv1/2D (io parallel / stream)
• EinsumDense (io parallel)

With this strategy, all matmul like operations in there layers are decomposed into optimized adder trees. Heavy lifting tasks are offloaded to da4ml, where everything is jitted with numba. There, CMVM problem is optimized with greedy common subexpression elimination. A reduction of LUT consumption of over 30% is frequently seen when WRAP is used as overflow mode with improved latency. DSP consumption will almost always be 0 with this strategy.

This PR depends on the s-quark-pr and includes all changes made there. (QEinsumDense not available otherwise)

Type of change

• New feature (non-breaking change which adds functionality)

Tests

Tests added to test_hgq_layers.py and test_einsum_dense.py. EinsumDense test will NOT be triggered in the current configuration due to keras v3 dependency.

Checklist

• No docs for now.