Advances in machine learning (ML) and the internet-of-things (IoT) have resulted in a renewed interest in analog matrix-vector multiplication (MvM) accelerators [1-3]. Classification based tasks have exploited low-to-medium resolution multiplication and accuracy boosting algorithms in order to compensate for the reduced resolution. Complementing classification, tasks like source separation and localization have diverse applications ranging from signal conditioning in communication  and ultrasound to electroencephalography (EEG)  source localization and spike sorting, and greatly benefit from similar algorithms. However, due to their lower resolution and limited channel count previously developed systems cannot be directly applied to this task. High-resolution analog multiplication introduces challenges that have limited prior work to less than 6-bit multiplication in the analog domain. Alternative approaches utilizing very high oversampling result in very inefficient solutions. High precision in matrix-multiplication can mitigate the effects of ill-conditioned (almost singular) matrices, as with beamforming separation of near-collinear sources, and other tasks incurring principal component analysis  or independent component analysis (ICA) . As seen in Fig. 21.7.1, a large signal dynamic range at the input can results in an untenable dynamic range specification on the downstream data-converters leading to greater than 10× increase in power . Thus, we present a multichannel multiple-input multiple-out (MIMO) mixed-signal linear transform system, with analog signal path and digital coefficient control, composed of an array of 14-bit Nested Thermometer Multiplying DACs (NTMDACs) implementing analog multiplication, and variable gain amplifier (VGA) implementing accumulation. We demonstrate state-of-the art performance on two tasks, spectrally oblivious interference suppression in communication signals and EEG signal separation.